Development, testing, and application of quantitative oxygenation
imaging from magnetic susceptibility by MRI
by
Audrey P. Fan
B.S., Stanford University, 2008
S.M., Massachusetts Institute of Technology, 2010
Submitted to the Department of Electrical Engineering and Computer Science in Partial
Fulfillment of the Requirements for the Degree of
sF
TrS INsQLOGY
DOCTOR OF PHILOSOPHY
JUN 10 20M
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2014
©2014 Massachusetts Institute of Technology. All rights reserved.
Signature of author
Signature redacted
Department of Electrical Engineering and Computer Science
May 21, 2014
Certified by
Signature redacted
Elfar AdalIsteinsson
Associate Professor, Electrical Engineering and Computer Science,
Health Sciences and Technology, Institute of Medical Engineering and Science
Thesis Supervisor
Accepted by
_______Signature redacted
Leslie A. Kolodziejski
I (2 J
and Computer Science
Professor of Electrical Engineering
Chairman, Committee on Graduate Students
Development, testing, and application of quantitative oxygenation
imaging from magnetic susceptibility by MRI
by
Audrey P. Fan
Submitted to the Department of Electrical Engineering and Computer Science in Partial
Fulfillment of the Requirements for the Degree of Doctor of Philosophy.
ABSTRACT
The healthy brain consumes 20% of total oxygen used by the body under normal
conditions. Continuous oxygen delivery to neural tissue is needed to maintain normal brain
function and viability. Reliable measurements of brain oxygenation can provide critical
information to diagnose and manage diseases in which this oxygen supply is disturbed,
including stroke and tumor. In acute stroke, for instance, metabolic biomarkers such as local
oxygen extraction fraction (OEF) have been shown to identify tissue at risk of infarction by
positron emission tomography. This knowledge can then be used to identify patients who are
candidates for reperfusion therapies or to avoid thrombolytic therapy in futile situations.
Unfortunately, there is currently no clinically feasible method for radiologists to assess brain
oxygenation in patients. My thesis aims to address this need through development of a clinically
viable tool to examine regional OEF in the brain with magnetic resonance imaging (MRI).
We have designed a novel imaging and analysis method to quantify oxygenation in cerebral
veins. MRI phase images are sensitive to local, oxygenation-dependent magnetic field
variations in brain vessels, due to the presence of paramagnetic deoxyhemoglobin molecules in
venous blood. Our method was developed on a 3 Tesla MRI scanner and tested in 10 healthy
volunteers during hypercapnia, i.e. breathing of low levels of C02. This respiratory challenge
changes the baseline oxygenation state of the brain, enabling us to test whether our MRI
method can detect different levels of OEF in vivo. We also show that OEF is reduced in 23
patients with multiple sclerosis, an autoimmune disease of the central nervous disease, and
relates to their performance on cognitive tasks.
Thesis Supervisor: Elfar Adalsteinsson
Title: Associate Professor, Department of Electrical Engineering and Computer Science,
Department of Health Sciences and Technology, Institute of Medical Engineering and Science
3
4
Acknowledgements
This page of the thesis was the certainly hardest for me to write.
To my adviser Elfar Adalsteinsson, thank you for welcoming me to the MRI Group. You've
bravely shepherded me, through dark times and good, and you've made me into a better
person.
Bruce Rosen, without you this thesis would literally not have possible. Thank you for your
creative insight, and for being my utmost champion.
Denny Freeman, you've been a rock and mentor throughout my time at MIT. Thanks for
teaching me to teach, and preparing me for "the beginning of the beginning" after graduation.
Ellen Grant and Karl Evans, your energy and kindness were my fuel through the last stretch of
the PhD.
Dwight Nishimura, you have supported me during college and throughout graduate school. I
can't wait to come home to Stanford and share with you the person I've become.
I appreciate Leslie Kolodziejski and Martha Gray for challenging me to look at the big picture,
and the impact I could make if I use my skills for good. I thank Arlene Wint, Linda Butler, Donna
Crowe, and Elizabeth Hoy for their radiant smiles and making my life so much easier.
My labmates Trina, Berkin, Itthi, Christin, Paula, Borjan, Jeff, Filiz, Adrien, Shaoying, Joonsung,
Kawin, Lohith, Obaidah, Patrick, Jean-Philippe, Bo, Shivang and German have seen me at my
worst, and yet still believe in me. I'm grateful for their companionship and intellectual vibrancy.
I thank my clinical collaborators in the MS group and the QTIM group and David Boas for giving
me a reason to go to work every day.
To my friends, what a wonderful time it's been! I'll miss the "get-fit-get-fat" escapades with Lori,
Chelsea, Yvonne, Tiffany, Tim, and James. I treasure the potlucks with my fam-bam, Ramesh,
Carrie, and William. John and Grace, you gave me a home, physically and emotionally, when I
needed it the most. Vineeta, you always made me feel that I could run the distance. Kristina,
perhaps I need you to help me express how much I cherish our friendship. I thank the lovely
ladies in EECS, Rose, Ermin, Shreya, Bonnie, Ying, and Lei for being my role models.
5
To my sisters Kathy, Denise, and Lillian, and my parents, your love is precious. This thesis is
dedicated to you.
6
Contents
Acknow ledgem ents .................................................................................................................
5
Figures.....................................................................................................................................11
Tables ......................................................................................................................................
15
1.
Specific Aim s ...................................................................................................................
17
2.
Background and Significance.....................................................................................
19
Clinical need for oxygenation imaging......................................................................
19
2.1.
2.2.1.
Acute ischem ic stroke and oxygenation imaging ..............................................
19
2.2.2.
Brain cancer and tumor hypoxia imaging ........................................................
23
2.2.3.
Metabolic changes in neurodegenerative diseases and normal aging..............25
2.3.
27
2.3.1.
Calibrated BOLD measurements of oxygenation during functional activity.....28
2.3.2.
Intravascular T2 relaxation measurements of oxygenation................................ 30
2.3.3.
Susceptibility weighted imaging of oxygenation ...............................................
32
S ig n ific a n c e ................................................................................................................
33
Development: Quantitative oxygenation venography from MRI susceptibility .....
35
2 .4 .
3.
Q uantitative M RI of Oxygenation ............................................................................
3 .1 .
A b s tra c t ......................................................................................................................
35
3 .2 .
In tro d u ctio n .................................................................................................................
35
3.3.
Materials and Methods............................................................................................
37
3.3.1.
Relationship between OEF and magnetic susceptibility ...................................
37
3.3.2.
Regularized approaches for quantitative susceptibility mapping ......................
39
3.3.3.
Numerical sim ulations.....................................................................................
40
3.3.4.
M RI Acquisition................................................................................................
41
3.3.5.
Quantitative susceptibility map reconstruction.................................................. 41
3.3.6.
Vessel graphing and display of quantitative OEF venograms..........................
3 .4 .
Re s u lts ........................................................................................................................
42
43
7
3.4.1.
Effect of et- versus e2- regularization on OEF ...............................................
43
3.4.2.
Comparison between OEF from MR susceptometry and QSM ........................
44
3.4.3.
OEF reconstruction profile across vessel tilt angle...........................................
48
3.4.4.
Quantitative oxygenation venograms in vivo....................................................
49
3 .5 .
D isc u s s io n ..................................................................................................................
3.5.1.
4.
51
Quantitative oxygenation venograms in healthy volunteers and comparison with
p re v io u s stu d ie s .................................................................................................................
51
3.5.2.
Limitations of the study ....................................................................................
53
3 .5 .3 .
C o n c lu s io n s .........................................................................................................
54
Testing: Regional quantification of cerebral venous oxygenation from susceptibility
during hypercapnia.................................................................................................................55
4 .1 .
A b stra ct ......................................................................................................................
55
4 .2 .
Intro d u ctio n .................................................................................................................
56
4.3.
Materials and methods...........................................................................................
58
4.3.1.
MRI acquisitions ...............................................................................................
58
4.3.2.
Gas manipulations ...........................................................................................
59
4.3.3.
Quantitative susceptibility mapping reconstruction...........................................
60
4.3.4.
Processing of arterial spin labeling scans ........................................................
61
4.3.5.
Quantitative measurements of brain physiology................................................62
4.3.6.
OEF predictions from blood flow changes........................................................
64
4.3.7.
Statistical methods...........................................................................................
64
4 .4 .
65
4.4.1.
Measurements of CBF and OEF during eucapnia and hypercapnia.................65
4.4.2.
Comparison of measured versus predicted OEF in various brain regions ......
4 .5 .
5.
Re s u lts ........................................................................................................................
D isc u s s io n ..................................................................................................................
65
69
4.5.1.
Vessel contrast during gas modulation.............................................................
69
4.5.2.
Regional OEF changes during hypercapnia....................................................
69
4.5.3.
Assumption of constant CMRO 2 during hypercapnia and potential pitfalls........70
4.5.4.
Future methodological improvements to oxygenation imaging ..........................
71
4 .5 .5 .
C o n c lu s io n s .........................................................................................................
73
Application: Quantitative oxygenation extraction fraction and reproducibility in
m ultiple sclerosis at 7 Tesla MRI susceptibility...............................................................
75
8
6.
5 .1 .
A b stra ct ......................................................................................................................
75
5 .2 .
Intro d u c tio n .................................................................................................................
76
5.3.
Materials and Methods............................................................................................
77
5.3.1.
Patients and Control Subjects..........................................................................
77
5.3.2.
Data Acquisition..............................................................................................
78
5.3.3.
Data Processing for OEF Quantification...........................................................
79
5.3.4.
MRI Characterization of Tissue Volumes, Lesion Volumes and Lesion Counts....81
5.3.5.
Neuropsychological Testing Methods ...............................................................
82
5.3.6.
Statistical analysis ...........................................................................................
82
5 .4 .
R e s u lts ........................................................................................................................
84
5 .5 .
D is c u s s io n ..................................................................................................................
88
5.5.1.
OEF findings in multiple sclerosis and reproducibility....................................... 88
5.5.2.
Lim itations of the work .....................................................................................
89
5.5.3.
Conclusions ....................................................................................................
90
Conclusions and Future Directions.............................................................................93
6.1.
Modeling the vasculature for improved accuracy of OEF imaging ...........................
93
6 .1 .1 .
Im p a ct..................................................................................................................9
6.1.2.
Proposed approach..........................................................................................
94
6.1.3.
Metrics of success ...........................................................................................
95
6.1.4.
Potential pitfalls and alternative strategies ......................................................
95
6.2.
3
Fast, efficient acquisition of susceptibility to enable a clinical oxygenation exam. ... 96
6 .2 .1 .
Im p a ct..................................................................................................................9
6.2.2.
Proposed approach..........................................................................................
97
6.2.3.
Metrics of success ............................................................................................
97
6.3.
6
High-resolution estimation of the cerebral metabolic rate of oxygen (CMRO 2 ) at 7 Tesla
98
7.
6 .3 .1 .
Im p a ct..................................................................................................................9
6.3.2.
Proposed approach..........................................................................................
98
6.3.3.
Metrics of success ...........................................................................................
99
6.3.4.
Potential pitfalls and alternative strategies ......................................................
99
References .....................................................................................................................
8
101
9
10
Figures
Figure 3.1. Susceptibility mapping results with el- and f2- regularization in numerical simulation
of a parallel vessel. The optimal regularization weighting parameters determined by the
discrepancy principle were k1 = 3.0x10-4 and X2 = 1.5x10-2 . Images are also shown for underregularized solutions with smaller regularization weighting than optimum ( 11 = 5.0x10-5 and 2 =
2.0x10-3 ); as well as over-regularized solutions with larger regularization weighting than
optim um ( 11= 3.0x10-3 and k2 = 0.2).....................................................................................
44
Figure 3.2. (Left) Plot of absolute OEF error in venous oxygenation (%) from QSM across
regularization weighting parameters in numerical simulation. At the optimal weighting of k1 =
3.Oxl 04, fl-regularized QSM resulted in 1.6% error. In contrast, at the optimal weighting of
1.5x1 0-2, F2-regularized QSM resulted in 6.9% error.............................................................
k2
=
45
Figure 3.3. (Right) The same sagittal slice is shown from in vivo susceptibility maps from -1Fand e2-regularized QSM. The susceptibility maps were created at the optimal regularization
parameters for the in vivo data, k1 = 4.5x1 0-4 and X2 = 1.Ox1 0-2. The zoomed inset highlights a
cortical pial vessel on the susceptibility maps. OEF from QSM in this cortical vein was 33.5%
and 29.7% for F1- and F2-regularization, respectively. .........................................................
45
Figure 3.4. Parallel vessel identified on the same slice in phase, field map, and susceptibility
images. The phase images at TE = 20.3 ms are shown after removal of background signal and
phase wraps with Hanning filter of width 96/512, 64/512, and 32/512 of the image matrix size
respectively. The field map image is shown after removal of undesired global fields estimated by
projection onto dipole fields (Liu et al., 2011 a). The susceptibility map was reconstructed with
fl-regularization at the optimal weighting of A1 = 4.5x1 04. The same cross-section of the
parallel vessel is show n in all insets (blue arrows). ...............................................................
46
11
Figure 3.5. Comparison of Sv0
2
=
1- OEF from MR susceptometry, with SvO 2 from QSM in 10
parallel vessels from one volunteer. Mean SvO 2 values from phase images corresponding to
different filter widths are statistically different with P-values < 10-2. However, there is no
detectable difference between mean SvO 2 from QSM and mean SvO 2 from MR susceptometry
a p p lie d o n th e fie ld m a p ............................................................................................................
47
Figure 3.6. Mean SvO 2 from QSM in numerical simulation across vessel tilt angle with respect to
the main field (Bo) for fl-regularized (A) and V2-regularized QSM algoirthms (B). The error bars
indicate standard deviation of estimated SvO 2 and the dotted line delineates the true simulated
value of 65% (dotted line). (C) Mean SvO 2 from QSM across vessel tilt angle observed in vivo
from one healthy volunteer. Each data point represents a SvO 2 measurement from a single
ve s s e l e d g e ...............................................................................................................................
47
Figure 3.7. Susceptibility map in one healthy volunteer thresholded at X > 0.15, and the
corresponding vessels that are graphed by the Volumetric Image Data Analysis software in
MATLAB (Tsai et al., 2009). In this volunteer, the venous vasculature is represented by a total
of 1090 edges inside the vessels. .........................................................................................
48
Figure 3.8. Quantitative oxygenation venograms which display baseline OEF along each vein in
three healthy volunteers. In the first subject, major veins in the brain are labeled, including the
superior sagittal sinus (SSS), inferior sagittal sinus (ISS), straight sinus (SS), transverse sinus
(TS), and superior anastom ic vein (SAV). ............................................................................
49
Figure 4.9. Physiological time courses of end-tidal C02 (ETCO 2) in mmHg and minute
ventilation in L/min for one healthy volunteer. Green regions indicate transition periods (-4
minutes) between eucapnia and hypercapnia, and blue regions indicate periods of stable
hypercapnia. As expected, ETCO 2 increased during hypercapnia and was associated with an
increase in m inute ventilation...............................................................................................
60
Figure 4.10. (a) Minimum intensity projection of gradient echo magnitude images and (b)
maximum intensity projections of quantitative susceptibility maps (ppm) over 20-mm
corresponding to eucapnia and hypercapnia in one volunteer. Notice the diminished vessel
contrast due to decreased venous blood susceptibility during the hypercapnic condition relative
to the eucapnic condition on both magnitude and susceptibility images. Yellow arrows indicate
individual vessels of interest including (1) the straight sinus, (2) the internal cerebral veins, (3)
occipital pial veins, (4) parietal pial veins, and (5) frontal pial veins. .....................................
62
12
Figure 4.11. Regions of interest (ROI) defined from Freesurfer
(http://surfer.nmr.mgh.harvard.edu) cortical segmentation for quantification of local cerebral
blood flow (CBF) on arterial spin labeling data in one healthy subject. .................................
63
Figure 4.12. Scatter plots across subjects of normalized % change in oxygen extraction fraction
(OEF) versus increase in end-tidal C02 (ETCO 2) in mmHg. The plots are generated separately
for (a) the straight sinus and internal cerebral veins draining deep gray matter; and (b) cortical
pial vessels draining surface gray matter. Linear fits are shown for each graph with slope (%
OEF / mmHg) indicating reactivity of vessel OEF to the hypercapnic challenge, and R value to
characterize the goodness of fit. ..........................................................................................
67
Figure 4.13. Scatter plots across subjects of measured versus predicted percent change of
oxygen extraction fraction (OEF) in (a) deep gray matter, and in (b) superficial cortical regions.
Measured OEF values (vertical axis) derive from susceptibility measurements in individual
veins; while the OEF predictions (horizontal axis) are determined solely by cerebral blood flow
(CBF) values from the arterial spin labeling acquisitions. Linear fits are shown for each graph
with slope with R value to characterize the goodness of fit.................................................... 68
Figure 5.14. Orientation and geometry of representative cortical vessel segment in a patient with
clinically isolated syndrome. (a) Sagittal views of magnitude (top) and filtered phase (bottom)
from the gradient echo acquisition. The rectangles highlight the vessel identified in (b), which
depicts the zoomed magnitude (left) and phase (right) of the vein. The double-sided arrow
indicates the segment of the vessel approximately parallel to Bo. (c) Axial view of magnitude
(left) and phase (right) of the same vessel, as indicated by the single-sided arrow. .............
80
Figure 5.15. Examples of the distribution of cortical vessels in various brain regions selected for
quantitative oxygen extraction measurements. Axial phase images are displayed after filtering
from a control subject (top) and a patient with secondary progressive MS (bottom). .............
80
Figure 5.16. Bland-Altman plots depicting (a) inter-observer and (b) inter-observer
reproducibility of mean cortical OEF made from the same data in 5 controls and 5 patients. (c)
Scatter plot depicting scan-rescan variability of mean cortical OEF in 5 separate healthy
subjects scanned twice in sessions a week apart. The diamonds show confidence intervals
based on the mean standard deviation of OEF across sessions computed in the group. ..... 86
13
Figure 5.17. Box-plot representation of OEF in 14 controls and 23 patients with MS in the (a)
sensorimotor cortex, (b) parietal cortex, (c) prefrontal cortex, and (d) averaged across the entire
cortex. The asterisks indicate significantly reduced OEF in all patients relative to healthy
controls by the Mann-Whitney test before correction for multiple comparisons..................... 87
Figure 5.18. Scatter plot of the correlation between mean cortical OEF with (a) processing
speed Z-score (p = 0.50, uncorrected P = 0.01), and with (b) executive function Z-score (p =
0.48, uncorrected P = 0.03). These relationships remained significant even after correction for
multiple comparisons by controlling the false discovery rate (corrected P < 0.04). ................ 87
14
Tables
Table 3.1. Mean absolute OEF (%) levels in major veins of the brain for three healthy volunteers
.................................................................................................................................................
50
Table 4.2. Mean and standard deviation of cortical physiological parameters measured by MRI
in each gas condition (N = 10)............................................................................................
. . 66
Table 5.3. Demographics and cognitive characteristics of 23 patients with multiple sclerosis... .78
Table 5.4. Table of References for Neuropsychological Test Normative Values............83
Table 5.5. Mean and standard deviation of OEF across the cortex (%) in reproducibility
a n a ly se s ....................................................................................................................................
85
Table 5.6. MRI characteristics of 23 patients with multiple sclerosis.............................. 85
Table 6.7. Accelerated acquisitions for QSM........................................................................
97
15
16
1. Specific Aims
The overall goal of this thesis is to develop magnetic resonance imaging (MRI) tools for
non-invasive, quantitative imaging of human brain oxygenation during normal physiology and in
neurological disorders such as stroke, tumor, and Alzheimer's disease. Venous oxygen
saturation (SvO 2 ) and Oxygen extraction fraction (OEF) in the brain are important indicators of
tissue health and viability. To date, established in vivo methods to measure these parameters
have relied on positron emission tomography (PET) imaging with
150
radiotracers. However,
150
PET is not clinically used because it requires injection of short-lifetime radiotracers, invasive
arterial sampling, and specialized equipment that is not widely available in hospitals. A regional,
quantitative MRI method to study brain oxygenation would offer non-invasive imaging with
improved resolution and scan time, and serve to replace
150
PET measurements of OEF.
We have developed new MRI technique, phase-based regional oxygen metabolism
(PROM), to quantify regional brain oxygenation (Fan et al., 2012). The approach measures local
OEF in individual vessels by direct quantification of the oxygenation-dependent magnetic
susceptibility shift between cerebral veins and surrounding brain tissue (Haacke et al., 1997a;
Weisskoff and Kiihne, 1992b). PROM uses standard sequences available on most clinical
scanners to acquire MRI phase images, and applies post-processing to extract relevant metrics
of physiology. Initial oxygenation measurements in healthy human volunteers from PROM MRI
studies fall in the normal physiological range identified by
150
PET studies in the literature (Fan
et al., 2012). Despite these promising findings, the method is limited to cerebral vessels with
specific geometry and orientation, and its reliability has not been characterized in different
oxygenation settings.
To make the MRI tool more broadly applicable in the brain, 3-dimensional susceptibility
distributions were reconstructed, from which absolute OEF can be visualized along the cerebral
venous vasculature. We tested the new method in human subjects at rest and during mild
hypercapnia, i.e. inhalation of low concentrations of C02, which provides known alterations the
17
global oxygenation state of the brain. After this validation, susceptibility-based oxygenation
imaging was applied to patients with multiple sclerosis (MS), an autoimmune disease of the
central nervous system. Subtle reductions in cerebral OEF have been observed in MS both from
PET and MRI studies (Brooks et al., 1984; Ge et al., 2012). The pilot study may further our
understanding of the pathophysiology underlying MS disease, and supports use of OEF imaging
as a novel metabolic biomarker to monitor MS evolution. The specific aims of this thesis are to:
Aim 1: Develop quantitative oxygenation venography from MRI. We demonstrated new
acquisition and processing methods to create oxygenation venograms that map absolute
OEF along the brain venous vasculature. State-of-the-art quantitative susceptibility mapping
(QSM) was used to reconstruct susceptibility values and estimate OEF in veins of arbitrary
orientation and geometry. OEF imaging was then implemented in vivo and compared to
values from previous phase-based MRI approaches. We also evaluated the performance of
the method for various vessel orientations with respect to the main magnetic field.
Aim 2: Test oxygenation imaging during hypercapnia. Here we tested whether the
susceptibility-based MRI approach can detect expected OEF changes during CO 2
inhalation. Healthy volunteers were scanned on a 3 Tesla MRI during eucapnic and
hypercapnic gas states. Local OEF changes between the two conditions were measured in
individual cerebral vessels, both in the deep gray matter and cortical gray matter. Assuming
no change in the underlying oxygen metabolism in each condition, regional changes in
perfusion were used to predict local oxygenation changes. We compared our OEF
measurements with these predictions to build confidence in the technique.
Aim 3: Apply oxygenation imaging to patients with multiple sclerosis. In collaboration
with Dr. Caterina Mainero at the Athinoula A. Martinos Center for Biomedical Imaging, we
compared brain oxygenation in patients at different stages of MS to age-matched controls.
We also evaluated the reproducibility of OEF measurements in both cohorts at 7 Tesla.
Finally, because the pathological and clinical correlates of altered oxygen metabolism are
not well understood, we evaluated quantitative OEF changes against MRI measures of
tissue damage and measures of clinical disability in patients.
The outcomes of this thesis are contributions toward a validated, quantitative MR-based
toolset to investigate human brain oxygenation in vivo. These tools will be significantly more
accessible to clinicians and researchers than PET-based methods, and may ultimately lead to
improved diagnosis and management of neurological disease including stroke and tumor.
18
2. Background and Significance
2.1.
Clinical need for oxygenation imaging
The healthy brain receives 15% of cardiac output and consumes 20% of total oxygen used
by the body under normal conditions (Gallagher et al., 1998; Magistretti and Pellerin, 1999).
Continuous oxygen delivery to neural tissue is necessary to maintain normal brain function and
viability. Consequently, oxygen extraction fraction (OEF) and the cerebral metabolic rate of
oxygen consumption (CMRO 2) are important indicators of tissue health in the brain. Noninvasive
imaging of brain oxygenation would provide new metabolic biomarkers to study cerebral
physiology at rest and during functional activity (Davis et al., 1998; Ito et al., 2005a).
Oxygenation imaging can also inform pathophysiological models and target therapies in brain
disorders with aberrant regional oxygenation, such as stroke (Geisler et al., 2006) and tumor
(Elas et al., 2003); as well as in neurodegenerative disorders with more subtle metabolic
changes, such as Alzheimer's disease (Hock et al., 1997) and multiple sclerosis (Ge et al.,
2012).
2.2.1.
Acute ischemic stroke and oxygenation imaging
Stroke is a common disorder and a major cause of death worldwide, leading to over 140,000
deaths each year in the United States alone. Currently, intravenous tissue plasminogen
activator (tPA) is the only medication approved by the Federal Drug Administration to treat acute
ischemic stroke patients. Because improved outcomes have only been demonstrated within 4.5
hours of symptom onset (Hacke et al., 2008), tPA treatment is limited to a mere 3.4 - 5.2% of
stroke patients (Adeoye et al., 2011). The effectiveness of delayed intervention beyond the 4.5hour time window depends on accurate identification of potentially recoverable tissue. The
therapeutic window likely varies between individuals depending on vascular anatomy, collateral
flow patterns, and comorbidities. Therefore, distinguishing the penumbra (Astrup et al., 1981;
Heiss and Graf, 1994; Hossmann, 1994) - under-perfused tissue with functional impairment
19
("misery perfusion") but maintained structural integrity - from irreversibly infarcted tissue per
patient is essential.
150
PET imaging has shown that early disturbances in blood flow and energy metabolism
during acute stroke inform the physiological state of affected tissue (Baron et al., 1981).
Simultaneous decreases in flow and oxygen consumption below threshold values indicate
permanent infarction, while flow decreases with preserved oxygen consumption indicate
penumbric tissue with a potential for recovery (Baron et al., 1981; Sobesky et al., 2005). PET
and MRI studies have also revealed minimum thresholds for oxygen metabolism (65
pmol/100g/min) and flow (20 ml/100g/min) that are needed to maintain tissue morphology
(Takasawa et al., 2008; Zaro-Weber et al., 2010a, b). These threshold values successfully
delineated necrotic versus potentially salvageable tissue in the infarct (Dani et al., 2011; Hakim
et al., 1987; Heiss et al., 2000; Wise et al., 1983). Absolute measurements of regional
oxygen consumption in the brain are thus important to identify the presence of viable
tissue to determine whether the patient is a good candidate for stroke therapy.
Although
150
PET can reliably identify penumbric tissue, its clinical use is limited by the
complexity, invasiveness, and radiation exposure of the methods (Heiss and Sobesky, 2008).
Previous
150
PET oxygenation studies in stroke have either used a continuous gas inhalation
approach (Baron et al., 1981; Kuwabara et al., 1998), which requires a complex gas delivery
system, or bolus administration of H2 15 0 and
1502
(Gibbs et al., 1984; lbaraki et al., 2004;
Sobesky et al., 2005). Each approach requires complex setup for sequential delivery of
radiotracers, and arterial blood sampling to determine an arterial input function for quantitative
measurements (Baron et al., 1989; Frackowiak et al., 1980; Herscovitch et al., 1985; Jones et
al., 1976; Mintun et al., 1984). Furthermore, because
150
markers have a half-life of only two
minutes, the PET scanner must be located near a cyclotron which creates the radiotracers.
These obstacles to
150
PET imaging have led to use of surrogate markers via other
modalities, including MRI, to identify penumbra in clinical practice.
In the past two decades, the mismatch between diffusion weighted imaging (DWI) and
perfusion-weighted imaging (PWI) has been widely used as a surrogate MRI biomarker to
identify the ischemic penumbra (Davalos et al., 2004; Rother et al., 2002; Schellinger et al.,
2003; Warach et al., 2012). DWI detects the irreversible infarct lesion area, while PWI detects
tissue with reduced perfusion. It has been suggested that the penumbra can be determined
from the diffusion-perfusion mismatch region (Albers et al., 2006; Kidwell et al., 2003;
20
Neumann-Haefelin et al., 1999). Several clinical trials for desmoteplase, a new thrombolytic
agent, in fact selected patients based on pre-randomized penumbral imaging with this mismatch
method (Furlan et al., 2006; Hacke et al., 2005; Hacke et al., 2009). However, several
assumptions of the DWI-PWI approach have recently been challenged. For instance, the initial
diffusion lesion may not consist only of permanently damaged tissue because the lesion can be
reversed if blood flow is restored early in the stroke (Chalela et al., 2004; Kidwell et al., 2000;
Parsons et al., 2002). Distinguishing true penumbra from oligemia (tissue with flow defects but
low risk of tissue damage) is also difficult, as PWI tends to overestimate the volume of tissue at
risk (Parsons et al., 2001). For these reasons, current DWI-PWI standards do not predict the
penumbra as accurately as 150 PET (Kajimoto et al., 2003; Kidwell et al., 2004; Sobesky et
al., 2005), and on average overestimate the penumbric volume by 66% (Sobesky et al., 2005).
Underlying challenges of DWI-PWI include the lack of quantitation from surrogate MRI markers
for oxygenation.
As an alternative to DWI-PWI, extravascular measurements of relaxation parameters in
brain tissue via MRI have been proposed to assess oxygenation in stroke. Because of its
sensitivity to changes of deoxyhemoglobin (dHb) concentration in tissue, known as the bloodoxygenation level dependent (BOLD) effect, T2 * relaxation is thought to closely reflect the
metabolic state of tissue (Bandettini et al., 1994; Boxerman et al., 1995). Elevated OEF in tissue
corresponds to higher dHb concentration and lower T2 *. This information could help distinguish
metabolically active and inactive tissues within hypoperfused brain regions. T2* hypointensities
in the affected hemisphere, consistent with elevated OEF, have been observed in the animal
models (De Crespigny et al., 1992; Roussel et al., 1995) and in small case studies of patients
with acute stroke (Morita et al., 2008; Tamura et al., 2002; Wardlaw and von Heijne, 2006).
However, T2 * signal did not correlate with gold-standard OEF measurements derived from PET
(Donswijk et al., 2009). This discrepancy may be due to background field inhomogeneities that
lead to underestimation of T2 * relaxation in focal regions not related to misery perfusion.
Furthermore, separating oxygenation-related T2 * signal from T2 relaxation changes due to
cerebral blood volume (CBV), vasogenic edema, or inflammation is challenging (Grohn et al.,
1998). As a result, preliminary T2 * imaging in patients has not provided clear conclusions
about the time course and spatial extent of oxygenation changes in stroke evolution.
Improvements to the relaxation approach have been proposed to better identify the
penumbra. For instance, T2' relaxation (1/T
2* =
1/T2
+
1/T 2 ') is more directly related to dHb
concentration and the volume fraction of dHb compared to T2 or T2 * relaxation (Yablonskiy,
21
1998). Geisler et al. found the lowest T2 ' (lowest OEF) in the irreversible diffusion lesion and
highest T2 ' (highest OEF) in the penumbral tissue (Geisler et al., 2006). T2' has also been used
to predict infarct growth to select patients for thrombolytic treatment (Siemonsen et al., 2008).
However, T2 ' measurement are not immune to artifacts due to field inhomogeneities, and can
increase in all ischemic regions, making it difficult to distinguish between the core, penumbra,
and oligemia based on T2' alone (Zhang et al., 2011). To overcome background susceptibility as
a potential confounder, other groups have instead measured changes in T2 * or T2' during a
100% oxygen challenge (OC) to the patient. During OC, higher signal change is expected in the
penumbra because of its higher baseline concentration of dHb (high OEF). This trend was
experimentally observed in rat models (Santosh et al., 2008). In a clinical trial of 35 patients with
stroke, however, there was large variability in T2 * OC responses across patients, and poor
spatial correspondence between regions with high OC responses and the DWI-PWI mismatch
(Dani et al., 2010). This finding is not surprising, given the intrinsic differences in physiology
between patients and additional variability in CBV, reperfusion status, and T1 relaxation due to
dissolved 02 in blood that confound the interpretation of the OC signal.
Recent studies have applied an advanced oxygenation MRI method, termed quantitative
BOLD (qBOLD), in the stroke setting. This approach incorporates a biophysical model of T2 *
spin dephasing to extract absolute oxygenation values per voxel (An and Lin, 2000; Yablonskiy
and Haacke, 1994). Rat studies applying qBOLD have revealed that absolute oxygenation is
lower in areas of final infarction and decreased over time within this region (An et al., 2009; Lee
et al., 2003). This observation is consistent with metabolically active penumbra that evolves into
the infracted core over time. Furthermore, in a prospective clinical study of 40 patients,
thresholds based on qBOLD maps outperformed DWI-PWI imaging in predicting ischemia (An
et al., 2013; Lee et al., 2013; Lin et al., 2013). Despite these promising results, the accuracy of
qBOLD inherently depends on reliable T2 * measurements. Consequently, qBOLD is still
sensitive to artifacts due to magnetic field inhomogeneities and edema-related changes in T2
signal, as in other relaxation methods. In practice, qBOLD also requires complex data analysis
that is sensitive to noise and imaging artifacts. This initial literature reveals the utility of
quantitative MRI for stroke evaluation, but also highlights the need for technical
developments for robust, reliable oxygenation imaging to replace DWI-PWI identification
of penumbra.
22
2.2.2.
Brain cancer and tumor hypoxia imaging
Malignant brain cancer is a common disorder affecting 7.3 out of every 100,000 people in
the United States, and leading to over 13,700 deaths each year nationwide. The presence of
hypoxia, or reduced oxygenation levels, in tumor tissue is known to affect the outcome of cancer
treatment with radiation (Churchill-Davidson et al., 1957; Deschner and Gray, 1959; Gray et al.,
1953; Warburg, 1956) and chemotherapy (Teicher, 1994; Teicher et al., 1990). Oxygenated
tumors are more sensitive to radiation-induced DNA damage during radiotherapy (Deschner
and Gray, 1959; Gray et al., 1953; Rockwell et al., 2009), so that the treatment is more effective.
As such, identification of tumor hypoxia has prognostic value for patient survival (Brizel et al.,
1999; Dunst et al., 2003; Nordsmark et al., 2005; Rudat et al., 2001) and local disease control of
head and neck tumors (Brizel et al., 1999; Nordsmark and Overgaard, 2004). In addition, new
treatments
have been proposed to re-sensitize cancerous tissue to radiotherapy with
concurrent delivery of gases with high oxygen content (Brown and Wilson, 2004; Rischin et al.,
2010; von Pawel et al., 2000), or to interfere with downstream molecular processes induced by
hypoxia in the tumor (Kung et al., 2000). For these reasons, a technique to image brain
hypoxia is of considerable clinical interest to monitor cancer therapies and detect tumor
recurrence (Tatum et al., 2006; Vaupel and Mayer, 2007).
The first direct
observation of hypoxia
in tumors
were
based
on polarographic
measurements of the partial pressure of oxygen (pO2) made with microelectrodes in tissue
(Braun et al., 2001; Vaupel et al., 2007; Young et al., 1996). Due to the invasive nature of this
technique, polarographic information is limited to superficial tumors and cannot be applied in the
clinic. The procedure could also alter the microenvironment in the tumor, leading to inaccurate
readings of oxygen concentration. As a result, practical biomarkers to characterize the
oxygenation status (Evans and Koch, 2003) and oxygenation heterogeneity within tumors
(Menon and Fraker, 2005) are currently unavailable.
To fulfill this need, novel PET tracers have been developed to noninvasively assess hypoxia
in tumors. Such hypoxia tracers include the metabolic compound [ 18F]-fluoromisonidazole (' 1 FMISO) (Krohn et al., 2008; Kubota et al., 1999; Lee and Scott, 2007; Rasey et al., 2000) and the
metallic complex
60 64 Cu-ATSM
(Dearling et al., 1998; Fujibayashi et al., 1997; Lewis et al.,
1999). The tracers undergo an intracellular metabolic reaction that directly depends on the level
of tissue hypoxia. This process enables identification of hypoxic tissue that comports well with
microelectrode PO2 measurements (Gagel et al., 2007; Lawrentschuk et al., 2005). Hypoxia
imaging with PET can help to distinguish between malignant and lower grade gliomas in the
23
brain (Hino-Shishikura et al., 2014; Hirata et al., 2012; Tateishi et al., 2013); and has prognostic
value in predicting response to radiation therapy (Hicks et al., 2005; Rajendran et al., 2006;
Rischin et al., 2006; Thorwarth et al., 2006).
However, PET imaging with hypoxia markers has not been widely adopted in clinical use
due to critical limitations, which make it difficult to selectively assess oxygen metabolism in
tumors (Mees et al., 2009). These limitations include slow tracer accumulation in tumors, and
nonspecific tracer binding, and incomplete understanding of measurement reproducibility. For
instance, a recent
18
F-MISO reproducibility study in 20 head and neck cancer patients found
substantial variability in spatial uptake of the tracer between scan sessions (Nehmeh et al.,
2008). Relative to histological measures, different analysis methods for 18F-MISO PET can also
lead to different interpretations of the presence of tumor hypoxia (Shi et al., 2012). More work is
thus necessary before single time-point PET images can be safely used to guide hypoxiatargeted radiotherapy. A small number of
150
PET studies have also attempted to directly
quantify cerebral rate of oxygen metabolism (CMRO 2) in cancerous tissue (Beaney et al., 1985;
Ito et al., 1982; Lammertsma et al., 1983; Leenders, 1994; Rhodes et al., 1983). Although this
early work demonstrated in vivo feasibility to detect baseline oxygen metabolic dysfunction in
tumor (Leenders, 1994; Rhodes et al., 1983), "0 PET is also not widely used due to the
complex experimental setup and short half-life of the tracers. These obstacles represent an
unmet need for reliable oxygenation imaging in tumors to monitor outcomes of cancer
therapy.
As an alternative to PET imaging, oxygen-sensitive MRI is attractive because it avoids the
need for an exogenous tracer to detect hypoxia. The feasibility of BOLD and tissue oxygen level
dependent (TOLD) MRI, a distinct oxygenation contrast based on T1 relaxation (O'Connor et al.,
2009a), have been demonstrated in patients with tumor. The appearance of tumor on these MRI
contrasts were investigated at baseline (Jiang et al., 2013; O'Connor et al., 2009b); and during
hyperoxic gas challenges (Alonzi et al., 2009; Jerome et al., 2014; Rijpkema et al., 2002; Taylor
et al., 2001). However, initial MRI studies in animal models and in patients have reported
conflicting findings. Some groups observed strong associations between T2* and independent
measurements of PO2 (Elas et al., 2003; Hoskin et al., 2007; McPhail and Robinson, 2010;
Punwani et al., 1998); and support the prognostic value of T2* MRI in tumor (Rodrigues et al.,
2004). On the other hand, other groups found no obvious link between T2* and known metrics of
tumor physiology (Baudelet and Gallez, 2002; Chopra et al., 2009; Christen et al., 2012b). This
debate in the literature speaks to the challenge in interpreting relative BOLD and TOLD signals,
24
and has motivated the simultaneous use of multiple MRI contrasts to assess tumor oxygenation
status (Burrell et al., 2013; Hallac et al., 2013; Remmele et al., 2013).
To directly measure tissue oxygenation, our collaborators adopted a calibrated BOLD MRI
technique to quantify absolute changes in CMRO 2 during hyperoxia gas challenge in patients
with glioblastoma (Chiarelli et al., 2007c; Davis et al., 1998). Kim et al. found that during
breathing of 100% 02, absolute CMRO 2 increased in cancerous tissue but not in healthy gray
matter (Kim et al., 2011 b), which was consistent with observations from
18
F-MISO PET (Kim et
al., 2011 a). The results indicate preserved oxygen metabolic machinery in tumors that can
potentially be manipulated with therapy (Kim et al., 2011b). At the same time, baseline
measurements of OEF or CMRO 2 have not been shown in tumors by MRI, and could solidify
interpretations of the calibrated BOLD results from Kim et al. This initial work reveals that
quantitative
MRI
is promising
for tumor
evaluation,
and
motivates
technical
developments to improve in vivo oxygenation imaging in tumors.
2.2.3.
Metabolic changes in neurodegenerative diseases and normal aging
In contrast to stroke and tumor, in which the oxygen supply to the brain is acutely disturbed,
many
neurodegenerative disorders exhibit subtle dysfunction in oxygen consumption.
Reproducible, quantitative imaging is necessary to detect these subtle metabolic changes, and
may offer new biomarkers to detect early brain pathophysiology and evaluate novel
pharmacological treatments for these diseases. For example, Alzheimer's disease (AD) has
growing incidence and socioeconomic impact in our aging population, but is difficult to detect in
its early stages. Although new treatment strategies aim to slow or prevent neuronal death in AD,
there are no established imaging markers to diagnose the disorder or reliably monitor drug
interventions (Mueller et al., 2005). As a result, AD is typically treated in later stages of
dementia such that drug therapy offers only small benefits.
To date, several studies have observed differences in brain oxygenation attributed to AD.
Hock et al. used near-infrared spectroscopy (NIRS) to non-invasively study changes in cerebral
hemoglobin oxygenation in the frontal and parietal cortex during performance a verbal fluency
task in patients compared to age-matched controls (Hock et al., 1997). In patients, NIRS
revealed a marked reduction of regional CBF and cerebral hemoglobin oxygenation during
activation in the parietal cortex, most likely driven by degenerating brain areas. This finding is
consistent with 150 PET measurements of decreased relative CBF and CMRO 2 in the frontal,
25
parietal, and temporal cortex in patients with senile dementia of Alzheimer's type (Tohgi et al.,
1998). In addition, the hemodynamic control of cortical oxygenation (measured by NIRS)
relative to cerebral blood flow velocity (measured by transcranial Doppler) was different in
patients with mild to moderate AD relative to healthy controls, suggestive of disease-related
disruptions to the cerebral microvasculature (van Beek et al., 2012). These changes are most
apparent in high-risk individuals, i.e. those with at least one copy of the apolipoprotien E F4
(APOE4) gene. For instance, a recent MRI study found that individuals with increased risk for
AD have elevated resting CBF in the medial temporal lobe (Fleisher et al., 2009). However, the
subsequent interpretation of the BOLD signal trends observed in high-risk populations and in rat
models remains challenging (Sanganahalli et al., 2013).
If a new, robust metabolic marker is developed, scientists would be able to
characterize AD through longitudinal observation of subtle oxygenation changes across
patient populations. In its own right, OEF measurements in AD may predict early cognitive
impairment before the onset of anatomical brain changes (Yamauchi et al., 1994). Clinicians
could also begin to tease apart whether hemodynamic disruptions are a cause or consequence
of neuronal dysfunction and structural changes in neurodegeneration, including amyloid-P
plaque deposition typical in AD disease (Vlassenko et al., 2010). Additional morphological
alterations in cerebral capillaries (Farkas and Luiten, 2001) and infarct-like lesions in white
matter have been described by pathological studies (Brun and Englund, 1986); but their
relationship to other pathophysiology is not well studied. This includes dysfunction along other
metabolic pathways including glucose utilization, which is also common in AD (Adriaanse et al.,
2014; Desgranges et al., 1998; Friedland et al., 1985). Improved understanding of this
pathology would lead to earlier diagnosis of AD and the potential for better clinical
outcomes with early intervention. Longitudinal investigations in other neurodegenerative
disorders; including Huntington's disease (Cepeda-Prado et al., 2012; Leenders et al., 1986),
Parkinson's disease (Beal, 2003; Karimi et al., 2008), and multiple sclerosis (Ge et al., 2012);
would similarly benefit from technical advances in oxygenation imaging.
To apply oxygenation biomarkers in cases of neurodegenerative disorders, it is important to
distinguish disease-related metabolic changes from those that occur in healthy aging. For
decades, hemodynamic changes in the brain have been known to accompany normal aging
based on the studies by Kety and Schmidt (Kety, 1956). The original work of Kety and Schmidt
found global declines in CBF and CMRO 2, as well as an increase of OEF with age by the nitrous
oxide technique. Developments in 150 PET imaging later made it possible to study subdivisions
26
in the brain, revealing regional decreases both of CBF (Borghammer et al., 2008; Leenders et
al., 1990; Lenzi et al., 1981; Pantano et al., 1984); and of CMRO 2 in aging populations
(Eustache et al., 1995; Ibaraki et al., 2010; Marchal et al., 1992; Takada et al., 1992; Yamaguchi
et al., 1986). In contrast, other authors found no changes or equivocal results for CBF and
CMRO 2 (Burns and Tyrrell, 1992; Meltzer et al., 2000; Pantano et al., 1984), and only one PET
study has replicated the increase of OEF with age first reported by Kety and Schmidt (Leenders
et al., 1990). Given these contradictory results, it remains unclear how the underlying neuronal
metabolic demand and the coupling between oxygen usage and CBF evolve with healthy aging.
The inconsistencies between studies may partially derive from poor spatial resolution (-16mm
isotropic resolution) in early PET imaging, such that signals from white matter, cortex, and
cerebrospinal fluid (CSF) cannot be resolved. These limitations prompted Aanerud et al. to reanalyze regional metabolic measurements with information from several recent studies
(Aanerud et al., 2012). However, much work remains to be done to distinguish the effects of
aging and inter-individual variability on these biomarkers. MRI measurements of oxygenation
would facilitate prospective, longitudinal designs in future studies of aging populations.
2.3.
Quantitative MRI of Oxygenation
Although there is a great unmet clinical need for robust and reliable imaging of brain oxygen
utilization, this measurement is technically challenging in vivo. Many early studies of
oxygenation disturbance in brain disease and during healthy aging relied on PET imaging, either
by
150
tracers or hypoxia tracers such as
18
F-MISO. PET remains the gold standard for
quantitative measurements of cerebral physiology, but has relatively poor spatial resolution
compared to other, more accessible modalities such as MRI. Furthermore, PET imaging
requires the use of ionizing radiation, such that repeated use to monitor patients during the
course of treatment is limited. In particular,
150
PET requires complex setup to image short half-
life tracers, and radiologists are unable to evaluate oxygenation in current medical routines.
MRI is also sensitive to the oxygenation-dependent effects of deoxyhemoglobin (dHb) on
tissue relaxation and intravascular phase signal. Hemoglobin is the predominant molecular
carrier of oxygen in the arterial blood supply, and in its deoxygenated state has a paramagnetic
effect which perturbs the local magnetic field (Pauling and Coryell, 1936). At high and medium
magnetic fields, the transverse relaxation rates T2 and T2* of water protons in blood are largely
determined by the oxygenation state of the hemoglobin, i.e. oxygen saturation (Atalay et al.,
27
1995; Silvennoinen et al., 2003; Thulborn et al., 1982). Due to this effect, T2*- and T2- weighted
MRI provide well-known blood-oxygen level dependent (BOLD) contrast, which has pervaded
the field of cognitive neuroscience for use in functional neuroimaging investigations (Bandettini
et al., 1997; Kwong et al., 1992; Ogawa et al., 1990). Functional MRI (fMRI) with BOLD contrast
is noninvasive and detects hemodynamic changes due to functional activity with high temporal
and spatial resolution.
However, the BOLD signal has limited specificity to brain function because the relationship
between T2* and blood oxygenation is confounded by macroscopic field inhomogeneities, water
diffusion, and the geometry and orientation of the underlying blood vessel network.
Furthermore, exact models that relate BOLD to underlying physiological parameters, including
OEF and CMRO 2, are complex at best. In fact, individuals with smaller baseline oxygenation
tend to have smaller BOLD signal. Lu et al. characterized inter-subject variability in BOLD and
found that every 10% difference in baseline oxygenation across individuals led to -0.4% and 30.0% variation in BOLD and CBF signal (Lu et al., 2008). Consequently, interpretations of
neural activity and brain oxygen utilization based on BOLD alone are rarely quantitative and can
be misleading (Christen et al., 2012b).
New approaches using MRI have been developed to exploit the effects of dHb for
measurement of absolute blood oxygenation. These techniques fall into three categories - (1)
calibration of the T2*-weighted magnitude (BOLD) signal to quantify changes in CMRO 2 during
functional activity; (2) measurement of T2 relaxation in isolated venous blood from tissue or in
large vessels; and (3) measurement from blood susceptibility on MRI phase images to quantify
oxygen saturation in cerebral veins.
2.3.1.
Calibrated BOLD measurements of oxygenation during functional activity
Calibrated BOLD MRI is a set of techniques to measure relative changes in CMRO 2 from the
BOLD and CBF signal during a functional task. During functional activation, CMRO 2 in brain
tissue increases to meet the increased energy demand of activated neurons. Calibrated BOLD
approaches model the resulting extravascular BOLD signal as a function of relative
change in CMRO 2 (Davis et al., 1998; Hoge et al., 1999). Use of this model requires estimation
of the "M" calibration parameter, which corresponds to the maximum achievable BOLD fMRI
signal change. The "M" parameter is typically measured through a gas challenge, most
commonly hypercapnia, in which the CO 2 content of the blood is increased.
28
During hypercapnia, it is assumed that CBF is measurably increased but that CMRO 2
remains constant, thereby providing ideal conditions from which to estimate "M". Hypercapnic
calibration of the BOLD signal has been explored via (a) breathing of C0 2-enriched air (Chiarelli
et al., 2007b; Kim et al., 1999; Stefanovic et al., 2004); (b) use of simple breath-hold tasks to
create hypercapnic stress (Kastrup et al., 1999a; Thomason et al., 2007); or (c) use of a
carbogen gas mixture with elevated C02 and 02 but without any nitrogen (Gauthier and Hoge,
2013; Gauthier et al., 2011; Macey et al., 2003; Vesely et al., 2001). In a study of visual
activation in 10 healthy subjects, calibrated BOLD measurements were stable across days from
the same individual, with a low coefficient of variation (COV) = 7.4% (Leontiev and Buxton,
2007). On the other hand, comparisons between different "M" calculation methods resulted in
different methodological recommendations. One investigation found that hypercapnia and
breath-hold challenges performed equally well to estimate "M" (Kastrup et al., 2001), while
others reported that mild hypercapnia provided the most robust calibration (Bulte et al., 2009).
Other studies explored the use of hyperoxic gas (increased 02 content) for calibration to achieve
increased signal-to-noise ratio (SNR) (Chiarelli et al., 2007c; Goodwin et al., 2009; Mark et al.,
2011); but this approach may not be robust to different baseline hematocrit and OEF levels
(Blockley et al., 2012).
Calibrated BOLD techniques suffer from several limitations that prevent their use in
the clinic. (1) The techniques do not offer absolute quantification of oxygen usage in the brain
and instead only measure relative changes in CMRO 2 during a functional task. (2) To estimate
CMRO 2 changes in this manner, a separate gas calibration experiment is necessary, throughout
which oxygen consumption in the brain is presumably constant. This isometabolic assumption
has been called into question, as some reports suggest a CMRO 2 increase (Horvath et al.,
1994; Martin et al., 2006; Yang and Krasney, 1995) or CMRO 2 decrease during hypercapnia
(Bolar et al., 2010; Xu et al., 2011; Zappe et al., 2008). (3) Furthermore, in the calibrated fMRI
model, changes in cerebral blood volume (CBV) are inferred from MRI perfusion measurements
via the flow-volume parameter a (Griffeth and Buxton, 2011). Literature values for a range
between 0.29 and 0.64 (Ito et al., 2003; Rostrup et al., 2005); and vary across different brain
regions (Mark and Pike, 2012), and between males and females (Ciris et al., 2013). Although a
direct measurement of CBV would improve the accuracy of calibrated BOLD, in vivo imaging of
CBV is in itself challenging. (4) Finally, there is evidence that the "M" parameter varies between
cortical and subcortical regions (Ances et al., 2008); as well as between different cortical areas
(Chiarelli et al., 2007a). For this reason, assumption of a constant value for "M" across the brain
29
may lead to errors in the calibrated BOLD model (Lin et al., 2008). Because of these potential
confounders, different calibrated fMRI approaches may be optimal in various settings, such that
widespread use of the technique is challenging.
To address some of the limitations of calibrated BOLD, alternative methods to analyze the
BOLD signal have recently been proposed. Separate groups have independently demonstrated
mapping of baseline, absolute OEF in human volunteers through use of a pair of respiratory
challenges (Gauthier and Hoge, 2012; Germuska and Bulte, 2014). These OEF maps can be
incorporated into the calibrated BOLD model to create a comprehensive mapping of vascular
and metabolic biomarkers (Gauthier et al., 2012). The use of multiple gas modulations,
however, may be prohibitive in clinical settings.
Separately, other groups proposed a quantitative BOLD (qBOLD) approach to map OEF,
through modeling the extravascular BOLD signal in each voxel in the presence of dHb (An and
Lin, 2000; He and Yablonskiy, 2007). This signal model has been tested in physical phantoms
(Pannetier et al., 2013; Sohlin and Schad, 2011), and agreed well with blood oxygenation levels
in venous blood drawn from the superior sagittal sinus in vivo of rats (He et al., 2008). In
addition, the qBOLD approach has been applied in humans to image brain oxygenation during
breathing of carbogen (An et al., 2012), and to perform oximetry in skeletal muscle (Zheng et
al., 2014). Although qBOLD can provide estimates of OEF maps in vivo, it relies heavily
on a complex signal model that is difficult to implement in practice. More work to
investigate geometry of the vessel network assumed in this model (Christen et al., 2012d) and
to improve the accuracy of OEF maps with additional information from MRI flow scans (Christen
et al., 2012c), are necessary before qBOLD can be broadly adopted.
2.3.2.
Intravascular T2 relaxation measurements of oxygenation
A distinct class of MRI methods for oxygenation imaging measures T2 relaxation from
intravascular signal. T2 relaxation in blood is influenced by the presence of dHb, and relates to
the underlying blood oxygenation level through a theoretical model (Oja et al., 1999; van ZijI et
al., 1998) that has been confirmed empirically in vitro (Krishnamurthy et al., 2013b; Silvennoinen
et al., 2003; Wright et al., 1991; Zhao et al., 2007).
T2-based oxygenation imaging enjoys high temporal resolution and to date is the most
commonly employed approach for absolute quantification of OEF. The most utilized of these
methods is T2-Relaxation-Under-Spin-Tagging (TRUST), which applies a magnetic spin labeling
30
scheme to isolate pure blood signal in the superior sagittal sinus. Since its recent introduction
into the field (Lu and Ge, 2008), TRUST MRI has been validated against pulse oximetry for
arterial blood signal (Lu et al., 2012), and has been optimized to provide fast, reproducible
measurements of global venous oxygenation, in a scan time of 1 minute 12 seconds (Liu et al.,
2013; Xu et al., 2012). Because of its short acquisition time and reliability, TRUST is suitable for
clinical use even in the most challenging applications (Liu et al., 2014).
The main obstacle to T2-based oxygenation imaging is the isolation of venous blood signal
for T2 measurements. Previous studies primarily focus on blood signal from large vessels
identified by visual inspection, for which partial volume effects with surrounding tissue are
minimal and there is sufficient SNR to robustly estimate robust T2 relaxation. Example vessels
include large draining veins, such as the superior sagittal sinus (Jain et al., 2012c; Lu and Ge,
2008) and the jugular veins of the neck (Qin et al., 2011); as well as medium-sized vessels in
the visual cortex identified via a fMRI activation task (Golay et al., 2001). Most T2-based
oxygenation imaging studies to date trade off the ability to spatially resolve OEF values
in the brain for clinically feasible scan times. As such, these methods (including TRUST)
only provide global OEF values and cannot localize regional aberrations of oxygen metabolism
in stroke and tumor. In response, Krishnamurthy et al. proposed a T2-based method that applies
bipolar gradients sensitive to flow velocity to isolate blood in smaller veins (Krishnamurthy et al.,
2013a). The work adopted fast echo-planar-imaging MRI to achieve a sagittal section with-inplane resolution of 0.72 x 2.41mm 2 , in a scan time of 7 minutes. This spatial resolution enables
OEF measurement in smaller pial veins with diameter as small as -2mm, and is a promising
extension of T2-based oxygenation imaging.
More sophisticated methodology to map OEF from T2 has been proposed by our group and
independently replicated. This novel approach, termed quantitative imaging of extraction of
oxygen and tissue consumption (QUIXOTIC), takes advantage of velocity-selective spin labeling
to isolate MR signal from postcapillary venular blood on a voxel-by-voxel basis (Bolar et al.,
2011). Not only is QUIXOTIC the first T2-based MRI approach to map OEF in cerebral tissue,
but in theory enables the user to designate the microvascular compartment from which
oxygenation values are derived. Guo et al. proposed a variation of the method, called velocityselective excitation with arterial nulling (VSEAN), to mitigate contamination of the venous blood
signal by CSF contributions and eddy current effects (Guo et al., 2014; Guo and Wong, 2012).
Despite the elegance of these methods, QUIXOTIC and VSEAN are inherently plagued by low
SNR because its OEF values derive from parenchyma with less than 5% blood by volume (Ito et
31
al., 2005b). Thus, regional mapping of absolute OEF with spin-tagging approaches remain
difficult without prohibitively long scan times to achieve adequate SNR, or relatively poor spatial
resolution.
2.3.3.
Susceptibility weighted imaging of oxygenation
Although not usually exploited, MRI phase signal provides excellent tissue contrast in the
brain, due to local field variations from paramagnetic substances such as dHb, myelin, and iron
(Duyn et al., 2007; Shmueli et al., 2009b). In the context of oxygenation imaging, MRI phase
allows for quantification of OEF in individual veins from the magnetic susceptibility shift between
vessels and brain tissue. This susceptibility shift is modulated by the presence of paramagnetic
dHb molecules, and directly relates to the oxygenation level of the vein (Weisskoff and Kiihne,
1992b). Susceptibility differences then lead to small field perturbations that ultimately manifest
as phase contrast from standard gradient echo scans.
In susceptibility-weighted imaging (SWI), gradient echo magnitude images are modulated by
a phase mask to enhance the contrast between tissues with different magnetic susceptibilities
(Haacke et al., 2004; Rauscher et al., 2005b; Reichenbach and Haacke, 2001). Because of its
phase contrast, cerebral veins are highly visible as dark structures on SWI images (Cho et al.,
1992) and change their appearance in different oxygenation states (Rauscher et al., 2005a).
This vessel contrast, especially in small veins, improves at high field because of (1) increased
SNR to achieve higher spatial resolution; and (2) increased susceptibility differences such that
suitable contrast is achieved at shorter echo times (Deistung et al., 2008; Koopmans et al.,
2008; Rauscher et al., 2008). The main benefit of SWI contrast is its simple and robust
acquisition, since the gradient echo sequence is already available on most clinical
scanners. For this reason, SWI has found clinical use in detection of cerebral microbleeds in
traumatic brain injury (Hammond et al., 2009a) and even in fetal imaging (Neelavalli et al.,
2014).
In addition to venous contrast, phase images have been analyzed to quantify absolute OEF
in cerebral veins. By modeling each vessel as a long cylinder approximately parallel to the main
magnetic field, there is a simple relationship between the measured phase difference between
blood and brain tissue to the underlying oxygenation in the vein (Fernandez-Seara et al., 2006;
Weisskoff and Kiihne, 1992b). This approach, known as MR susceptometry, is easy to
implement and provides simple calibration of susceptibility differences to absolute OEF via
32
reference phase values typically identified in the cerebral parenchyma or CSF. Recently, MR
susceptometry has been applied to study oxygenation in large draining veins of the brain
(Fernandez-Seara et al., 2006; Jain et al., 2010); locally in smaller pial vessels of the cerebrum
(Fan et al., 2012; Haacke et al., 1997a); and even in skeletal muscle (Langham et al., 2009a;
Zheng et al., 2014). Global oxygenation measurements in the sagittal sinus were obtained in 30second scans, with low COV of 2.3% between scans. In addition, susceptibility-based OEF
values have been combined with MRI flow measurements from arterial spin labeling (Fan et al.,
2012) and phase-contrast imaging (Jain et al., 2011b; Rodgers et al., 2013) to assess global
CMRO 2 in the brain.
Unfortunately, the accuracy of oxygenation values from MR susceptometry depends heavily
on the vessel orientation and morphology. Recent investigations have considered realistic
vessel geometries to investigate the effect of vessel tilt angle and cross-section on absolute
oxygenation estimates (Langham et al., 2009b; Li et al., 2012). These simulations revealed
good OEF agreement with expected values in near-parallel vessels after correction for vessel
tilt. However, greater than 10% absolute OEF error was found for tilt angles of 400 or greater
relative to BO. As a result, phase-based OEF measurements are only available from large
vessel segments within a limited range of orientations that prevents general use of the
technique across the brain. A processing method to estimate OEF from susceptibility in an
arbitrary vessel must be developed before phase-based oxygenation imaging can be broadly
applied in the clinic.
2.4.
Significance
This thesis describes a new set of acquisition and processing techniques to map
quantitative oxygenation along the cerebral venous vasculature. The approach takes advantage
of the susceptibility differences between veins and brain tissue that present on MRI phase
images to quantify OEF (Section 2.2.3). In addition, the new method was designed to: (1)
provide regional OEF information from individual veins; and (2) be feasible within a clinically
viable imaging time. Before it can be applied, the new oxygenation imaging approach must be
tested in different, known physiological states and its reproducibility across scan sessions and
observers must be shown. If we are successful, this research would have the following impact
on a broad range of neurological disorders and functional neuroscience:
33
*
Fulfill a clinical need for robust assessment of brain oxygenation to manage diseases in
which this metabolic information is critical. One example is acute ischemic stroke, which is
the 3 rd leading cause of death in the United States and often results in debilitating cognitive
and physical impairment. The effectiveness of stroke therapies such as reperfusion of at-risk
brain areas depends heavily on accurate identification of potentially recoverable tissue, or
the penumbra. This penumbra is known to be identified by the presence of elevated oxygen
extraction in the brain. Unfortunately, radiologists currently lack the ability to image
oxygenation in these patients. Our technique would dramatically improve management of
acute stroke and evaluation of treatment efficacy to improve clinical outcomes.
*
Enable new understanding of the biology underlying many neurological diseases. For
instance, Alzheimer's disease currently can only be confirmed post-mortem as there are no
established imaging markers to diagnose the disorder. As a result, AD is typically treated in
later stages of dementia after the therapeutic window of new drugs. With new metabolic
markers such as OEF imaging, scientists can better characterize AD through longitudinal
observation of more subtle oxygenation changes across patient populations. Improved
understanding of this pathology would lead to earlier diagnosis of AD and the potential for
better clinical outcomes with early intervention.
*
Provide a new tool to investigate neurovascular coupling and map brain function. Functional
MRI (fMRI) with BOLD MRI has revolutionized studies in the cognitive sciences. Nearly all
fMRI techniques target the hemodynamic response to neural activation and use BOLD
signal as a surrogate of synaptic activity. However, the BOLD signal alone is difficult to
interpret without additional information about blood flow and oxygenation in the brain.
Currently, neurovascular coupling during brain activation is incompletely understood and
remains a central question of basic neuroscience. OEF imaging represents a fundamental
physiological parameter that is complementary to traditional BOLD scans. With absolute
quantification of oxygen metabolism, we can better understand baseline oxygen exchange
in the brain and its modulations during functional activation.
34
3. Development: Quantitative oxygenation venography
from MRI susceptibility
3.1.
Abstract
Contribution: Development of new acquisition and processing methods for quantitative
oxygenation venograms that map in vivo oxygen extraction fraction (OEF) along the cerebral
venous vasculature.
Regularized quantitative susceptibility mapping (QSM) is used to reconstruct susceptibility
values and estimate OEF in veins. QSM with -e- and e2-regularization are compared in
numerical simulations of vessel structures with known magnetic susceptibility. Dual-echo, flowcompensated phase images are collected in three healthy volunteers to create QSM images.
Bright veins in the susceptibility maps are vectorized and used to form a 3-dimensional vascular
mesh, or venogram, along which to display OEF values from QSM.
Quantitative oxygenation venograms that map OEF along brain vessels of arbitrary
orientation and geometry are shown in vivo. OEF values in major cerebral veins lie within the
normal physiological range reported by
150
PET. OEF from QSM is consistent with previous MR
susceptometry methods for vessel segments oriented parallel to the main magnetic field. In
vessel simulations, -e-regularization results in less than 10% OEF absolute error across all
vessel tilt orientations and provides more accurate OEF estimation than e2-regularization. The
proposed analysis of susceptibility images enables reliable mapping of quantitative OEF along
venograms and may facilitate clinical use of venous oxygenation imaging.
3.2.
Introduction
Gradient echo MRI can be used to quantify venous oxygen extraction fraction (OEF) in
individual veins from the magnetic susceptibility shift between vessels and brain tissue. This
35
susceptibility shift is modulated by the presence of paramagnetic deoxyhemoglobin molecules,
and through the blood hematocrit relates to the oxygenation level of the vein (Weisskoff and
Kiihne, 1992b). Previous MRI studies have modeled cerebral veins as long cylinders to quantify
blood oxygenation from T2* signal decay profiles internal and external to the vessel (Dagher and
Du, 2012; Sedlacik et al., 2007, 2009), as well as from phase signal differences between the
vein and tissue (Fernandez-Seara et al., 2006; Weisskoff and Kiihne, 1992b). Advantages of the
phase-based approach, known as MR susceptometry, include use of gradient echo acquisitions
that are readily available on most scanners and self-calibration to absolute OEF via reference
phase values in cerebral tissue.
Recently, MR susceptometry has been applied to study oxygenation in large draining veins
of the brain (Fernandez-Seara et al., 2006; Jain et al., 2010) and locally in smaller pial vessels
(Fan et al., 2012; Haacke et al., 1997a) that are parallel to the main field (Bo). In addition,
susceptibility-based OEF has been combined with MRI flow measurements from arterial spin
labeling (Fan et al., 2012) and phase-contrast imaging (Jain et al., 2011b; Jain et al., 2010) to
assess the cerebral metabolic rate of oxygen consumption. Other studies have also considered
the effect of vessel tilt angle and cross-section on oxygenation estimates (Langham et al.,
2009a; Li et al., 2012). Although these simulations revealed good OEF agreement with
expected values in near-parallel veins after correction for vessel tilt, nearly 40% absolute OEF
error was found for tilt angles of 50* or greater relative to B0 (Li et al., 2012). As a result, clinical
application of phase-based OEF imaging is currently restricted to vessel segments within a
limited range of orientations that prevents use of the technique across the brain.
To address these limitations, we propose to measure oxygenation directly on quantitative
susceptibility mapping (QSM) images reconstructed from MRI phase images. From QSM,
susceptibility values are available along any vein without cylinder orientation assumptions,
enabling OEF estimation in a larger set of vessels. Susceptibility mapping has been developed
to assess iron deposition (Bilgic et al., 2012; Schweser et al., 2011b), probe the anisotropic
structure of white matter tracts in the brain (Liu et al., 2012a), and characterize cerebral
pathology including lesions (Schweser et al., 2010) and microbleeds (Liu et al., 2012c). QSM
reconstruction is challenging because k-space information of the observed field map is innately
undersampled or damped due to nulls and small values in the dipole kernel near the magic
angle (54.70) (Marques and Bowtell, 2005; Salomir et al., 2003), such that recovery of the
underlying susceptibility is ill-posed. Current QSM approaches condition the inversion problem
of estimating magnetic susceptibility from MRI phase by k-space thresholding of large values in
36
the deconvolution kernel near the magic angle (Shmueli et al., 2009a; Wharton et al., 2010);
collecting multiple sets of phase data where the subject is placed in different physical positions
between scans (Liu et al., 2009; Schweser et al., 2011b); or applying mathematical
regularization through use of priors on the expected susceptibility distribution (Bilgic et al., 2012;
de Rochefort et al., 2010b; Liu et al., 2012b; Schweser et al., 2012). These QSM methods
present different artifact and noise properties (Liu et al., 2011 b), and careful selection of QSM
reconstruction settings is necessary for accurate OEF measurements.
In this work, we propose a new method to analyze and visualize susceptibility maps for
robust OEF estimation in veins across the brain. The reconstruction process combines QSM
with vascular graphing routines originally developed for high-resolution optical imaging of
microvasculature (Tsai et al., 2009). Cerebral veins in QSM maps are vectorized into a
representation of nodes and edges, such that OEF values can be averaged along physiological
vessel segments for increased signal-to-noise (SNR). Importantly, the graph structure also
allows for evaluation of the fidelity of oxygenation measurements across various tilt angle
orientations of cerebral veins in vivo. Through this approach, quantitative oxygenation
venograms that map OEF along each vessel are shown in healthy volunteers at 3 Tesla.
3.3.
Materials and Methods
3.3.1.
Relationship between OEF and magnetic susceptibility
The proposed method estimates OEF from magnetic susceptibility measurements in venous
blood. The susceptibility shift between venous blood and water
(AXvein-water)
is dominated by the
oxygenation-dependent concentration of paramagnetic deoxyhemoglobin molecules in blood.
This susceptibility difference is related to OEF in the vessel as (Weisskoff and Kiihne, 1992b):
AXvein-water
AXdo
Hct +
AX 0xy-water
[Eq. 1]
- OEF - Hct
where hematocrit (Hct) is the percent of blood that consists of erythrocytes,
AXd,
is the
susceptibility shift per unit hematocrit between fully oxygenated and fully deoxygenated
erythrocytes, and AXoxy-water is the susceptibility shift between oxygenated blood cells and water.
In this work we will interchangeably use OEF and SvO 2 =
1-
OEF.
37
Here
AXdo
is assumed to be 0.27 ppm (cgs) for calibration of OEF values, as previously
done for femoral veins (Langham et al., 2009a) and large, draining brain vessels (Jain et al.,
2010). The value was first reported by Spees et al. (Spees et al., 2001) and was recently
corroborated in an independent MRI study (Jain et al., 2012b). However, this assumed AXdO is
different from earlier reported values of 0.2 ppm (Plyavin and Blum, 1983; Thulborn et al., 1982)
and 0.18 ppm (Weisskoff and Kiihne, 1992b), which have also been used to calibrate OEF
measurements (Fernandez-Seara et al., 2006; Haacke et al., 1997a). The current paper adopts
AXdo =
0.27 ppm from the more recent studies, which address several potential sources of
measurement error in earlier work, such as erythrocyte settling within stationary samples. It is
noted that use of the earlier value
AXdo =
0.18 ppm leads to higher OEF estimates by -13%
absolute oxygenation (Fan et al., 2012). In contrast, oxygenated blood exhibits a much smaller
diamagnetic shift of
AXoxywater =
-0.03 ppm (Weisskoff and Kiihne, 1992b), and given a
physiological Hct of 40% (Guyton and Hall, 2000), the susceptibility contribution of oxygenated
erythrocytes
(-0.01
ppm)
is
small
compared
to
the
paramagnetic
shift
driven
by
deoxyhemoglobin.
As
magnetic
susceptibility
measurements
are
intrinsically
relative,
reproducible
quantification of OEF from blood susceptibility requires a reference with respect to a standard
tissue region. In previous work, venous susceptibility has been referenced to neighboring brain
tissue (Fan et al., 2012; Haacke et al., 1997a). However, this approach does not account for
regional variations in tissue susceptibility between gray and white matter (Duyn et al., 2007) or
increased susceptibility in iron-rich structures of the basal ganglia (Bilgic et al., 2012). In this
study, blood susceptibility is instead referenced to cerebrospinal fluid (CSF), by assigning Oppm
to the mean susceptibility of the anterior portion of the ventricles (Li et al., 2011; Yao et al.,
2009). Care was taken to avoid voxels near paramagnetic choroid plexus structures that could
distort reference susceptibility values (Deistung et al., 2013).
Although
susceptibility imaging can provide physiological information
about oxygen
saturation in veins, there is no MRI contrast mechanism to directly measure susceptibility.
Instead, the underlying susceptibility distribution (x) of the brain, if placed in a strong magnet,
induces field perturbations through a complex and nonlocal relationship (Marques and Bowtell,
2005). In practice, MRI is sensitive to the resulting field distribution (B), which manifests on
gradient echo phase images as
=
y7 -B-TE. Here, y is the proton gyromagnetic ratio and TE is
the echo time.
38
To simplify the estimation of susceptibility from phase for the purpose of OEF
measurements, MR susceptometry studies have modeled vessel segments as infinite cylinders.
For veins approximated as long cylinders approximately parallel to Bo, a simple analytical
relationship exists between the local field and blood susceptibility in the vein:
ABvein-water
1
41
Vvnwater - (3cos 2 0 vein
-
1) - BO
[Eq. 2]
where Ovein denotes the vein tilt angle relative to BO. The majority of MR susceptometry literature
investigates oxygenation in veins parallel to the main field (Fan et al., 2012; Fernandez-Seara et
al., 2006; Haacke et al., 1997a; Jain et al., 2010), although a few have also considered
perpendicular geometries (Dagher and Du, 2012; Sedlacik et al., 2009).
3.3.2.
Regularized approaches for quantitative susceptibility mapping
As an alternative, this work proposes to measure OEF through reconstruction of 3D
quantitative susceptibility maps, from which blood susceptibility and oxygenation level can be
directly read along cerebral veins. Instead of applying a cylinder model for cerebral vessels, the
new method makes prior assumptions about spatial variations in the underlying susceptibility
distribution. Regularization is a mathematical technique to incorporate such prior information to
solve an ill-posed problem, and several regularized approaches have been explored to
reconstruct susceptibility maps from single-orientation field maps (Bilgic et al., 2012; de
Rochefort et al., 2010b; Kressler et al., 2010; Liu et al., 2012b).
In this work, the regularization terms impose prior beliefs on the spatial gradients of
susceptibility. For instance, the -e-norm penalty term promotes a sparse number of non-zero
spatial gradients in X, such that the optimal X is favored to be piecewise constant within
anatomical tissue boundaries. The -e-regularized optimization problem is:
X* = argminx 1| b - F-DFX ||2 + Al -| GX |1,
where 11Y112
=
[Eq. 3]
lyy and I|yII1 = Z'yi. Here G = [Gx Gy Gz]T is the gradient operator and Xk is a
weighting parameter that trades off between data consistency (first term) and imposed spatial
prior (second term). The argmin notation stands for argument of the minimum, i.e. the optimal
susceptibility distribution which minimizes the value of the expression.
39
In contrast, the e2-norm penalty promotes a slowly-varying, smooth X solution with a large
number of small gradient coefficients. The e2-regularized method solves the following
optimization problem:
x* = argmin 11b - F- 1 DF X||1
+
A
-| |GX 11
[Eq.4]
This study compares - 1- and e2-regularization in the context of quantifying susceptibility shifts
within narrow vessel structures.
3.3.3.
Numerical simulations
To assess the fidelity of OEF measurements from QSM, numerical phantoms were
generated with known susceptibility values in vessels. The susceptibility phantoms were
simulated with matrix size = 240 x 240 x 154; spatial resolution = 1 x 1 x 1 mm 3; brain anatomy
based on the SR124 brain atlas (Rohlfing et al., 2010); and susceptibility values of -8.995, 9.045, and -9.04 ppm respectively for gray matter, white matter, and CSF (Duyn et al., 2007).
To minimize phase wrapping due to bulk susceptibility interfaces, voxels outside of the brain
were assigned susceptibility values identical to gray matter. Veins were approximated as
cylinders with 2-mm radius and length-to-radius ratio of 20, with susceptibility values
corresponding to OEF = 35% and Hct = 40%.
Local field maps were simulated from the constructed susceptibility distributions through
multiplication with the dipole kernel in k-space (Marques and Bowtell, 2005). To avoid aliasing,
each susceptibility map was padded to 480 x 480 x 308 matrix size with gray matter values prior
to convolution. Phase images were then simulated from the field maps for TE = 20 ms and field
strength of 3 Tesla, while the associated magnitude signal was assumed to be uniform across
the brain. Gaussian noise was added to the real and imaginary part of the complex signal to
achieve SNR of 35.6, which was the mean SNR observed across the brain in a healthy subject
at TE = 20 ms. We note that in practice, venous blood signal experiences faster T 2* decay (-25
ms) relative to the surrounding brain tissue (-56 ms) such that SNR levels would not be
spatially uniform as in the simulations. After addition of noise, the phase images were rescaled
into field maps that served as the numerical input into the QSM algorithm.
Initial simulations with a parallel vessel compared - 1- and e2 - regularized QSM for 46
regularization parameters, chosen logarithmically between 10-6 and 102. The optimal weightings
X, and X2 were selected by the discrepancy principle. The discrepancy principle is a heuristic
40
approach that identifies the optimal k for which the squared residual of the data consistency
term in the optimization (Eq. 3 and 4) matches the noise variance of the data (Hansen, 1998).
Finally, numerical simulations were repeated for vessel tilt angles ranging from 0-90* relative to
BO, in intervals of 50, through modifying the vessel model in object space while maintaining the
same vein length and susceptibility values.
3.3.4.
MRI Acquisition
Experiments were performed on a Siemens 3 Tesla MAGNETOM Trio a Tim System with a
32-channel receive head coil. Three healthy volunteers (2 female and 1 male, ages 25-26
years) were scanned with written consent under the local Institutional Review Board.
We implemented a dual-echo gradient echo sequence with flow compensation along all
spatial axes at each echo (Deistung et al., 2009b). High-resolution gradient echo scans were
acquired: repetition time (TR) = 26 ms; TE = 8.1, 20.3 ms; matrix size = 384 x 336 x 224;
resolution = 0.6 x 0.6 x 0.6 mm 3; flip angle = 15*; bandwidth (BW) = 260 Hz/pixel; GRAPPA
acceleration factor = 2; phase partial Fourier = 75%; acquisition time (TA) = 15:42 min.
Separate low-resolution, single-echo gradient echo scans were also collected with the same
spatial coverage: TR = 15 ms; TE = 6-10 ms spaced by 1 ms; matrix size = 128 x 112 x 56;
resolution = 1.8 x 1.8 x 2.4 mm 3 ; FA = 15*; BW = 260 Hz/pixel; TA = 1:20 min per echo. No
shimming was performed between the scans, and uncombined magnitude and phase images
were saved for all acquisitions.
3.3.5.
Quantitative susceptibility map reconstruction
The RF phase offset map corresponding to TE = 0 was estimated separately for each
receive channel from the five acquired echoes at the lower resolution (Robinson et al., 2011).
The estimated offset maps were subtracted from each receive channel of the 0.6-mm resolution
data before coil combination with weighted averaging (Hammond et al., 2008). This process
was performed independently to generate 0.6-mm isotropic resolution phase images,
ctTE2,
PTE1
and
at TE = 8.1 and 20.3 ms respectively.
After coil combination,
4
TE1
and
4
TE2
were unwrapped via FSL Prelude in 3D (Jenkinson,
2003) with use of brain mask defined from the magnitude images by the FSL Brain Extraction
Tool (Smith, 2002b). Unreliable voxels with nonlinear phase evolution across echo times were
41
identified as high spatial frequency structures on the phase offset map
$tTE1) -TE
1
c
1
0,hires =
4
TE1 -
/ (TE 2-TE 1) (Schweser et al., 2011b). A voxel was considered unreliable if
4
(
TE2 -
I$OhireS -
$O,smoothl exceeds pi/4 radians, where $N,smooth represents a 4th-order polynomial fit to the high-
resolution $O,hires map. The corrupt voxels constituted 2.1% of total brain voxels on average and
were removed from the brain mask for all subsequent processing.
For each volunteer, a field map estimate was calculated as B =
cITE2 /
(y-TE 2-BO). Background
field contributions were estimated by 100 iterations of the projection onto dipole fields (PDF)
routine (Liu et al., 2011a) for removal, resulting in a local field map as input into the QSM
algorithm. The optimal A parameters were determined in one subject via the discrepancy
principle, based on the noise variance
Gscaled2 =
[ a / (A - y - Bo - TE - 10-6) ]2 where
C2
is the
underlying standard deviation of noise estimated from the complex signal at TE = 20.3 ms; and
A is the mean magnitude intensity across the brain for the subject. Only the -e-regularized
susceptibility maps were further processed to create venograms, and the same optimal
X, weighting was used across all volunteer datasets. CSF susceptibility was averaged for each
volunteer from manually drawn ROls (mean volume = 4.8 ± 1 cm 3), and subtracted from the
QSM map.
3.3.6.
Vessel graphing and display of quantitative OEF venograms
Each in vivo susceptibility map was thresholded at X > 0.15 ppm to preserve venous
structures with relatively high susceptibility values. The Volumetric Image Data Analysis (VIDA)
software, originally developed to vectorize 3D vascular volumes from optical imaging for
quantitative analysis, was adapted to graph vessels in the thresholded X maps (Tsai et al.,
2009). The output of VIDA is a graph representation of the venous vasculature as nodes (placed
inside the vessels) and edges (between the nodes to indicate connected vessel segments).
Vessel diameter was automatically estimated at each node as described in (Fang et al., 2008).
Manual editing was performed to adjust node positions that did not accurately align with
vasculature, and to adjust vessel diameter for larger veins if underestimated by the automated
procedure.
The graphed output was then rendered into a volumetric mesh, or venogram, which displays
the venous vasculature in 3D with appropriate thickness (Fang and Boas, 2009). Cylindrical
ROls were created around each vessel edge based on the edge diameter. To minimize partial
42
volume effects,
Xvein
was estimated for each vessel edge based on the maximum value of the
corresponding cylindrical ROI. All oxygenation values made in vivo assumed Hct = 40% and
0.27 ppm. For increased SNR, OEF values were averaged along each connected vein
segment.
AXdO =
3.4.
Results
3.4.1. Effect of -e- versus
e2 - regularization
on OEF
In numerical simulation, the optimal regularization weightings determined by the discrepancy
principle was k1 = 3.0x10
4
and k2 = 1.5x10 2 for -e- and e 2 - regularized QSM, respectively.
Figure 3.1 illustrates the simulated vessel on reconstructed susceptibility maps for the underregularized (A smaller than optimum), optimally-regularized, and over-regularized (A larger than
optimum) solutions. Figure 3.2 displays absolute error in OEF values directly reconstructed from
the QSM maps against various A. Note that both QSM algorithms show similar profiles in Figure
2, with largest OEF error corresponding to large A. At the optimal regularization weighting, 4based QSM resulted in 1.6% absolute OEF error relative to the true value of 35% in the vein;
while e2-based QSM resulted in 6.9% absolute OEF underestimate due to underestimation of Y.
This finding is consistent with literature reports of susceptibility underestimation with e2regularized QSM algorithms (Liu et al., 2011 b).
Similar in vivo values for A1 = 4.5x10-4 and A2
=
volunteer. A sagittal slice from the corresponding -e- and
1.0x10-2 were identified from the first
-e2-
regularized QSM maps is depicted
in Figure 3.3. This slice contains a cortical pial vein, for which OEF values were directly
estimated
AXvein-CSF.
As in the numerical phantom, 4i-regularized QSM provided a higher OEF of
33.5% in this vein compared to OEF of 29.7% estimated by e2-regularized QSM. Based on
these initial experiences, further venogram processing was performed on 4e- instead of e2 regularized QSM images to avoid potential OEF underestimation as observed in the phantom.
43
Underregularization
Optimal
regularization
Overregularization
Figure 3.1. Susceptibility mapping results with -e- and e2 - regularization in numerical simulation of
a parallel vessel. The optimal regularization weighting parameters determined by the discrepancy
2
principle were 1 = 3.0x10-4 and k 2 = 1.5x10- . Images are also shown for under-regularized
3
5
solutions with smaller regularization weighting than optimum ( 1 = 5.0x10- and k 2 = 2.0x10- ); as
3
well as over-regularized solutions with larger regularization weighting than optimum ( 1 = 3.0x10and k 2 = 0.2).
3.4.2.
Comparison between SvO 2 from MR susceptometry and QSM
SvO 2 values were measured directly on susceptibility maps and compared to SvO 2 values
from model-based MR susceptometry. The SvO 2 comparisons were made on 10 parallel vessel
segments manually identified from an in vivo dataset acquired at TE = 20.3 ms. Vessels
included in the analysis were visible in at least three consecutive axial slices and were viewed in
the sagittal and coronal slices to confirm their orientation relative to B0. The same regions of
interest (ROls) identified in each vessel and in CSF were used for all measurements.
44
40 t
30
Lu
0
c')l
20
2 10
II
0
Iog( )
-6
-5
-4
-3
-2
-1
0
40
L
~30
~
to
0 20
0 6.10
0F
-6
4
-2
0
2
Figure 3.2. (Left) Plot of absolute OEF error in venous oxygenation (%) from QSM across
regularization weighting parameters in numerical simulation. At the optimal weighting of , = 3.0x10~
4, t1 -regularized QSM resulted in 1.6% error. In contrast, at the optimal weighting of k2 = 1.5x10~
2, e2 -regularized QSM resulted in 6.9% error.
Figure 3.3. (Right) The same sagittal slice is shown from in vivo susceptibility maps from l- and
e2 -regularized QSM. The susceptibility maps were created at the optimal regularization parameters
for the in vivo data, k, = 4.5xl04 and k2 = 1 .0x10 2 . The zoomed inset highlights a cortical pial vessel
on the susceptibility maps. OEF from QSM in this cortical vein was 33.5% and 29.7% for e- and e2 regularization, respectively.
The phase volume was processed using homodyne filtering on a slice-by-slice basis (Wang
et al., 2000a). The Hanning filter widths were calculated to be 96/512, 64/512, and 32/512 of the
image matrix size to match typical SWI processing parameters. Figure 3.4a-c displays the axial
phase images after filtering and highlights a sample parallel vein used in the analysis. Phase
measurements from the filtered images were modeled with a parallel cylinder to estimate OEF
(Eq. 2). The mean OEF values corresponding to different filter widths were statistically different,
with P-values significant at the 0.01%
level after Bonferonni adjustment for multiple
comparisons (Figure 3.5).
45
Figure 3.4. Parallel vessel identified on the same slice in phase, field map, and susceptibility
images. The phase images at TE = 20.3 ms are shown after removal of background signal and
phase wraps with Hanning filter of width 96/512, 64/512, and 32/512 of the image matrix size
respectively. The field map image is shown after removal of undesired global fields estimated by
projection onto dipole fields (Liu et al., 2011a). The susceptibility map was reconstructed with -eregularization at the optimal weighting of Al = 4.5xlO-4. The same cross-section of the parallel vessel
is shown in all insets (blue arrows).
Additional oxygenation measurements were made through application of MR susceptometry
(Eq. 3) on the local field map after PDF removal of global fields. The mean OEF value across
vessels modeled from the local field map (34.2% ± 4%) was not statistically different from the
mean OEF value directly measured from the QSM map (33.9% ± 5%) at the 5% significance
level (P = 0.91, paired T-test with correction for multiple comparisons). The variance of OEF
estimates were also not different across the phase, field map, and QSM techniques (P = 0.63 by
Bartlett's test for equal variances). Furthermore, Xwater estimates from ventricle ROls in the QSM
maps were stable across subjects (mean values of 0.001 ± 0.05, 0.003 ± 0.04, and 0.016 ± 0.05
ppm respectively), without large filter-dependent variations in CSF values observed in SWI
homodyne filtering.
46
80
P<104
T
75
70
IA
(0
p<10
T
Figure 3.5. Comparison of SvO 2 = 1- OEF
from MR susceptometry, with SvO 2 from
QSM in 10 parallel vessels from one
volunteer. Mean SvO 2 values from phase
images corresponding to different filter
widths are statistically different with P-values
< 10-2. However, there is no detectable
difference between mean SvO 2 from QSM
and mean SvO 2 from MR susceptometry
applied on the field map.
4
* I~.
65
~~
- -
60
I
1
-
-T
55
(N = 96)
90
Phase
(N 64)
Fleidmap
90
A
85
QSM
(N a 32)
85
80
Sol
64 75
75
(0
70
65
65
60
0
10
20
40
30
50
70
60
80
90
C
0
10
20
30
40
50
60
70
80
90
Angle with B0 (degrees)
Angle with B0 (degrees)
80
60
e2
SD
~ornan-
75
-
0,70
60
55
mean - SD
0
10
20
30
40
50
60
70
80
90
Angle with B0 (degrees)
Figure 3.6. Mean SvO 2 from QSM in numerical simulation across vessel tilt angle with respect to
the main field (Bo) for el-regularized (A) and e2 -regularized QSM algoirthms (B). The error bars
indicate standard deviation of estimated SvO 2 and the dotted line delineates the true simulated value
of 65% (dotted line). (C) Mean SvO 2 from QSM across vessel tilt angle observed in vivo from one
healthy volunteer. Each data point represents a SvO 2 measurement from a single vessel edge.
47
Figure 3.7. Susceptibility map in one healthy volunteer thresholded at X > 0.15, and the
corresponding vessels that are graphed by the Volumetric Image Data Analysis software in MATLAB
(Tsai et al., 2009). In this volunteer, the venous vasculature is represented by a total of 1090 edges
inside the vessels.
3.4.3. SvO 2 reconstruction profile across vessel tilt angle
SvO 2 estimated from el- and e2-regularized QSM is plotted across vein tilt angles for
numerical simulations (Figure 3.6a,b). Both algorithms resulted in similar SvO 2 profiles over tilt
angle, with most severe OEF underestimation at approximately 450 relative to the main field. For
all vessel orientations, 42-regularized QSM resulted in greater underestimation of mean SvO
2
relative to e-regularized QSM (P <
10 4,
paired T-test for each tilt angle). The error in mean
SvO 2 measured by the e-based algorithm was less than 10% absolute oxygenation for all
vessel tilt angles. For 15/18 of the investigated vessel orientations, 2-regularized QSM
exhibited greater SvO 2 variance relative to er-regularized QSM (P < 0.05, F-test for variance
equality).
48
Figure 3.8. Quantitative oxygenation venograms which display baseline OEF along each vein in
three healthy volunteers. In the first subject, major veins in the brain are labeled, including the
superior sagittal sinus (SSS), inferior sagittal sinus (ISS), straight sinus (SS), transverse sinus (TS),
and superior anastomic vein (SAV).
3.4.4. Quantitative oxygenation venograms in vivo
Brain vessels were graphed from thresholded QSM maps for each volunteer with default
VIDA parameters, including rod filter half-size of 5 voxels (Figure 3.7). Across all subjects, the
venous vasculature was represented on average by 1032 ± 70 edges inside the vessels. The
average length of an edge was 2.7 ± 0.1 mm, and OEF values were averaged along vessel
segments of mean length 23.5 ± 2 mm. The resulting quantitative oxygenation venograms that
display absolute OEF are shown in Figure 3.8. Mean OEF across all graphed vein segments,
was 37.5% ± 10%, 33.3% ± 7%, and 38.2% ± 10% respectively for each subject. OEF for
49
individual major cerebral vessels identified on the venograms ranged between 24.7% and
52.3% (Table 3.1). Significant OEF differences were found between individual veins (P < 10-4,
one-way analysis of variance across subjects). For instance, the superior anastomic vein was
found to have lower OEF relative to the straight or transverse sinuses, which may indicate the
presence of residual partial-volume effects in the method.
Because the vessels were graphed from the volunteer QSM maps, it was also possible to
probe SvO 2 across vessel tilt angle relative to BO in vivo. The vein tilt angle was determined for
each vessel segment from the spatial coordinates of its two end nodes. The SvO 2 values
measured in vivo for one volunteer are plotted versus tilt angle for all vessel edges in Figure
3.6c. OEF values ranged between 35.7% and 30.9% over all vein tilt angles in this subject.
Table 3.1. Mean absolute OEF (%) levels in major veins of the brain for three healthy
volunteers
Vein
Superior Sagittal Sinus
Subject 1
Subject 2
Subject 3
Mean across
subjects
38.2
9
32.3
6
38.0 ± 10
36.2± 3
37.3
10
32.4
10
38.0 ±12
35.9± 3
52.3
11
46.6
4
43.8 ±3
47.6± 4
43.4 ±6
40.0
6
41.4 ±8
41.6± 2
Transverse Sinus (Left)
(TS-L)
48.3 ± 6
42.4 ± 6
49.3 ± 10
46.7 ± 4
Superior Anastomic Vein
(SAV)
24.7 ±4
29.8 ±5
29.0
27.3± 3
(SSS)
Inferior Sagittal Sinus
(ISS)
Straight Sinus
(SS)
Transverse Sinus (Right)
(TS-R)
7
50
3.5.
Discussion
We have introduced a new method to reconstruct quantitative oxygenation venograms that
display OEF values along the venous vasculature of the human brain. The proposed method
applies QSM analysis onto MRI phase images, which are sensitive to oxygenation-dependent
variations of magnetic susceptibility in venous blood. For this study, we demonstrate feasibility
of the technique in healthy subjects at 3 Tesla. Mean cerebral OEF at baseline in these
volunteers ranged from 33.3% to 38.2%, which is consistent with recent OEF measurements of
39.0% ± 6% and 43.0% ± 6% by autoradiographic (Hattori et al., 2004) and dynamic PET
imaging, respectively (Bremmer et al., 2011).
3.5.1.
Quantitative oxygenation venograms in healthy volunteers and comparison
with previous studies
Previous studies have used phase images to quantify OEF through modeling brain vessels
as long, parallel cylinders (Fan et al., 2012; Fernandez-Seara et al., 2006; Haacke et al., 1997a;
Jain et al., 2010; Weisskoff and Kiihne, 1992b). While this susceptometry approach is relatively
straightforward to implement, reliable OEF estimation in practice depends on accurate
knowledge of vessel tilt and manual identification of vessel segments for which the infinite
cylinder model is appropriate. For these reasons, the set of brain vessels amenable to oxygen
saturation measurements via MR susceptometry is limited. To achieve orientation-independent,
3D reconstruction of veins, Haacke et al. instead applied inverse filter truncation to SWI phase
images after high-pass filtering (Haacke et al., 2010). This literature described the effect of
vessel size on vein contrast in the susceptibility images and found physiological reasonable
OEF in vessels that were 8 mm or 16 mm in size. In contrast, our study proposes to reconstruct
comprehensive oxygenation venograms through use of regularization with prior information
imposed on the spatial gradients of
.
One challenge with the proposed technique is selection of the regularization weighting
parameter X for accurate OEF estimation. Under-regularized solutions may present increased
noise level and streaking artifacts values near the air-tissue interfaces, while over-regularization
may result in an overly smoothed susceptibility map that underestimates Xvein, resulting in nearly
40% absolute error OEF for large A values. In practice, because the ground truth susceptibility
distribution is unknown, selection of A necessarily relies on heuristic approaches. Here the
discrepancy principle was used to identify the optimal parameters of X, = 4.5x10-4 and A2
=
1.0
51
x10-2
for e- and e2 -regularized QSM in vivo. These values are similar to X, = 2.0x10-4 and
X2 =
1.5x10-2 identified via a distinct heuristic known as the L-curve approach (Hansen, 2000) in a
separate study from our group (Bilgic et al., 2012).
In previous work with large anatomical structures such as the basal ganglia, the choice of
versus
-e
e2 norm
did not heavily influence susceptibility values for iron quantification (Bilgic et al.,
2012). However, we detected lower OEF in vessels from -e2-regularized QSM relative to 4regularized QSM, both in numerical simulation and in vivo. This finding suggests that the
smooth QSM solution promoted by e2-regularization is a suboptimal prior compared to the
piecewise-constant solution promoted by e-regularization for susceptibility quantification in
narrow vascular structures. We also observed that -regularization
provided smaller OEF
variance for the majority of vessel tilt orientations in simulation compared to e2-regularization.
This study also compared OEF values from the new QSM approach with traditional MR
susceptometry methods in the literature. One drawback to homodyne filtering of phase images
used in past studies is the effect of filter length on oxygenation values. Not surprisingly, we
found that mean OEF estimated from phase was statistically different for various Hanning filter
lengths, which comports with previous work attributing up to 12% OEF variation to filter size
differences (Langham et al., 2009b). In this work, background field was removed via the PDF
algorithm, which is expected to provide more accurate, parameter-independent removal of
global field compared to homodyne filtering. Model-based OEF values from the resulting local
field map were not statistically different from OEF values directly calibrated from the QSM maps.
These results suggest that the new QSM method is consistent with previous MR susceptometry
approaches for oxygenation imaging in vessels parallel to Bo.
Furthermore, marked improvement in OEF accuracy was achieved for veins that were not
parallel to B0 through use of QSM instead of MR susceptometry. One major benefit of vessel
graphing in the study was the ability to probe OEF values versus vessel tilt angle in vivo. Mean
OEF in vivo varied between 30.9% and 35.7% across all vessel tilt angles, which is consistent
with less than 10% error over all vessel tilt angles expected from our numerical simulations with
4-QSM. OEF estimates from QSM are thus more robust across vessel orientation than MR
susceptometry, which exhibits nearly 40% error for vessel tilt angles near the magic angle (Li et
al., 2012). As such, QSM enables oxygenation assessment over the full range of vessel tilt
angles, which was previously unattainable by MR susceptometry.
52
3.5.2.
Limitations of the study
Nonetheless, our numerical simulations suggest residual OEF bias from QSM at some
vessel tilt angles, with peak at 450 relative to B0. This residual bias may occur for vessel
orientations that contain considerable Fourier energy at the nulls and small values of the dipole
kernel. In the future, we will explore the incorporation of priors based on vascular anatomy into
the QSM reconstruction to reduce this observed bias. For this purpose, the proposed vessel
graphing may provide valuable 3D vascular models directly from bright veins in the QSM maps.
Our technique will benefit from improvements to graphing routines, such as pre-processing of
MR data with contrast enhancement methods, which may also reduce the need for manual
editing to achieve reliable venograms.
We note that in this work, hematocrit values were assumed to be uniformly Hct = 0.40 for
each subject. Hematocrit is widely variable between individuals, and typical values range
between 0.41 and 0.46 (Reichenbach and Haacke, 2001). For the in vivo susceptibility values
estimated in this study, this variability corresponds to estimated mean OEF range between
35.4% (for low hematocrit) and 31.6% (for high hematocrit) across the volunteers. Additionally,
there is a considerable gender difference in Hct, with normal values ranging between 0.41 0.54 for males and between 0.37 - 0.47 for females (Reichenbach and Haacke, 2001). In future
work, this parameter may be measured in each subject using a finger prick blood draw for more
accurate oxygenation estimates.
Another limitation of the study is potential partial volume effects between narrow veins and
the surrounding tissue, which may persist despite our use of the maximum value in each
cylindrical vessel ROI to estimate Xvein. Partial voluming can lead to underestimation of AXvein-water
from the QSM map, and thus underestimation in OEF. This effect may explain why OEF in the
smaller superior anastomic vein tended to be lower than OEF in larger draining veins such as
the sagittal sinus (Table 1). Partial voluming may also explain much of the variation in OEF at
each tilt angle for the in vivo plot from Figure 6b. In future work, we propose use of vascular
anatomical priors with more accurate estimates of vessel diameter to explicitly model and
minimize partial volume effects.
The proposed method take advantage of higher SNR at ultra-high field such as 7 Tesla to
achieve resolutions of 0.4 mm isotropic or smaller (Deistung et al., 2013) for OEF analysis in
smaller cerebral veins that are more indicative of regional brain oxygen metabolism. Maximum
intensity projections of QSM maps at 7 Tesla have been presented as a potential approach to
53
visualize and probe blood oxygenation in small brain vessels (Christen et al., 2012a). However,
long acquisition times (-16 minutes) and QSM reconstruction times (-1 hour per dataset)
currently preclude efficient application of our method to large matrix sizes. Acquisition of larger
datasets at 7 T will require more efficient sampling to maintain high spatial resolution with
shorter scan times (Wu et al., 2012b). Similarly, fast reconstruction at the MRI scanner is
necessary for clinical settings and can be accelerated with use of graphical processing cards
(Abuhashem et al., 2012b).
For the new technique to be clinically applicable, further work is necessary to assess the
source of variability in tracked vessel locations and OEF values between subjects. For instance,
the venogram structures in Figure 3.8 varied between the three volunteers and it is unclear
whether these differences are normal or can attributed to limitations in vessel tracking routines.
To address this question, QSM venograms may be compared with standard venography MRI
methods such as time-of-flight and phase-contrast scans in future work. Furthermore, spatial
variations in OEF were found across the venograms. This observation is not expected because
previous PET studies revealed fairly uniform oxygenation levels across the brain (Ishii et al.,
1996). Non-physiological sources of this variability may include imperfect vessel tracking,
suboptimal extraction of Xvein values, partial volume effects, and residual angle-dependent bias
in OEFand should be addressed in future work.
3.5.3.
Conclusions
We have proposed a novel method for quantitative oxygenation venography from QSM. Our
study presented physiologically meaningful OEF values for individual vessels, including large
draining veins such as the superior sagittal and transverse sinuses; as well as smaller pial veins
such as the superior anastomic vein. The MRI acquisition for the new method is related to SWI
scans which have already found use in the clinic (Hammond et al., 2009b; Idbaih et al., 2006;
Sehgal et al., 2005b). In addition to SWI magnitude contrast for veins, our method can provide
baseline OEF values along the venous vasculature from the same dataset. If current limitations
with the method are successfully overcome, quantitative oxygenation venograms may have
direct impact in clinical management of disease such as acute stroke and brain tumor; and may
provide earlier biomarkers for diagnosis of neurodegenerative disorders such as MS,
Alzheimer's and Huntington's Diseases.
54
4. Testing: Regional quantification of cerebral venous
oxygenation from susceptibility during hypercapnia
4.1.
Abstract
Contribution: Oxygenation imaging from magnetic susceptibility in MRI is a promising new
technique to quantify regional oxygen extraction fraction (OEF) along the cerebral venous
vasculature. However, this approach has not been tested for various oxygenation levels of the
brain. This study aims to test susceptibility imaging of oxygenation for different global brain
states created by a gas manipulation task.
Hypercapnia, via inhalation of 5% to 7% CO2, is a common respiratory challenge that
causes significant vascular changes to the brain, including a dramatic decrease in OEF. The
primary goal of this study was to test whether susceptibility imaging of oxygenation can detect
OEF changes induced by hypercapnia for five brain regions selected a priori. We acquired
gradient echo phase images (-6 min per scan) in ten healthy volunteers at 3 Tesla to measure
OEF in individual vessels during eucapnia and hypercapnia. In the deep gray matter, -42.3%
relative OEF decrease was measured in the straight sinus, and -39.9% OEF decrease in the
internal cerebral veins. Relative OEF decreases of approximately -50% were observed in pial
vessels draining each of the occipital, parietal, and frontal cortical areas.
Across volunteers, OEF changes in each brain region related to changes in end-tidal C02
(ETCO 2) between the two gas conditions. Mean OEF in the brain changed by 6.2 i 1% per
mmHg increase of ETCO 2. Independent flow measurements were also collected with pseudocontinuous arterial spin labeling MRI during eucapnia and hypercapnia. Regional perfusion
values in the brain were used to predict the local change in OEF, assuming no change in
cerebral oxygen consumption during hypercapnia. Measured OEF reductions from vessel
susceptibility correlated with predicted QEF changes in all brain regions. These findings suggest
that susceptibility imaging of oxygenation with MRI provide reliable OEF measurements with
high fidelity to underlying physiology in the brain.
55
4.2.
Introduction
Under normal conditions, the healthy human brain receives 15% of the cardiac output and
consumes 20% of total oxygen used by the body (Gallagher et al., 1998; Magistretti and
Pellerin, 1996). There is an unmet clinical need for a neuroimaging method to provide repeated,
non-invasive and reliable measures of regional brain oxygen utilization. Such measurements
could inform pathophysiological models and target therapies in brain disorders with aberrant
regional oxygenation, such as stroke (Geisler et al., 2006) and tumor (Elas et al., 2003); as well
as in neurodegenerative disorders with more subtle metabolic changes such as Alzheimer's
disease (Hock et al., 1997) and multiple sclerosis (Ge et al., 2012). Although there is a clinical
need for robust and reliable measurements of brain oxygen utilization, this is technically
challenging in vivo.
Positron emission tomography (PET) provides regional quantitation of brain oxygen
extraction fraction (OEF) and the cerebral metabolic rate of oxygen consumption (CMRO 2)
through use of 150 tracers. However, the need for specialized equipment, radiation exposure,
and invasive arterial sampling limit the clinical utility of 150 PET. Several magnetic resonance
imaging (MRI) methods have been recently proposed to quantify cerebral oxygenation from
magnetic susceptibility (Fan et al., 2012; Rodgers et al., 2013), T2 relaxation measurements in
venous blood (Krishnamurthy et al., 2013a; Lu and Ge, 2008), and calibrated blood-oxygenlevel-dependent (BOLD) signal changes (Blockley et al., 2012; Gauthier et al., 2012). T2 relaxation-under-spin tagging (TRUST) MRI provides measures of cerebral OEF through
estimates of T2 relaxation of venous blood in large veins, e.g. the superior sagittal sinus (SSS).
Although TRUST has been validated against pulse oximetry (Lu et al., 2012) and optimized for
reproducible, fast measurements of OEF (Liu et al., 2013), the technique is limited to global
measures of oxygen extraction. Our group recently proposed a method to map absolute OEF
along the cerebral venous vasculature from quantitative susceptibility mapping (QSM)
reconstructions (Fan et al., 2013). This susceptibility-based approach inherently provides
measures of regional cerebral OEF information within individual vessels, but remains to be
tested in different oxygenation states as induced by common physiological challenges.
Carbon dioxide (CO2) is an effective vasodilator and offers the ideal cerebrovascular
challenge to test our oxygenation venography method. Hypercapnia, via inhalation of 5% to 7%
inhalation, causes significant vascular changes to the brain. These changes include robust
increases to cerebral blood flow (CBF) between 35% to 50% (Kety and Schmidt, 1948; Sicard
C02
56
and Duong, 2005); increased cerebral blood volume (Ito et al., 2003); and higher blood
concentrations of C02 and 02. Hypercapnia has been considered a purely vascular challenge in
that small or negligible changes in the cerebral metabolic rate of oxygen consumption (CMRO 2),
a surrogate for neural activity, have been observed in studies of hypercapnia despite dramatic
cerebrovascular modulations (Chen and Pike, 2010). This effect has been demonstrated by
invasive studies (Kety and Schmidt, 1948) as well as non-invasive imaging studies (van ZijI et
al., 1998), and is supported by Fick's principle of arterio-venous difference that implies that
CMRO 2 is proportional to CBF and to OEF.
The assumption of constant CMRO 2 during hypercapnia forms the basis for previous studies
of: 1) cerebrovascular reserve (Bright et al., 2011; de Boorder et al., 2004), i.e. the reactivity of
vessels to the gas challenge, as a measure of hemodynamic health; and 2) calibration of the
BOLD signal to estimate changes in CMRO 2 during a functional task (Bulte et al., 2009; Davis et
al., 1998). In addition, several BOLD MRI studies have indirectly observed minimal changes in
CMRO 2 during hypercapnia (Rostrup et al., 2000; Sicard et al., 2003), which implies a decrease
in OEF proportional to the increase in CBF during hypercapnia. Thus, hypercapnia reliably
produces a predictable, global change in brain oxygenation which is detectable on MRI images
that are sensitive to magnetic susceptibility. For instance, venous vessels appear dark due to
the presence of paramagnetic deoxyhemoglobin (dHb) molecules on susceptibility-weighted
images (SWI), and the loss of vessel contrast observed during hypercapnia is consistent with
lower dHb concentration and decreased OEF (Rauscher et al., 2005a; Sedlacik et al., 2008).
Jain et al. went further to directly measure field shifts induced by the underlying changes in dHb
concentration during C02 inhalation from 2-dimensional, susceptibility-weighted phase images
(Jain et al., 2011b). This study quantified a 13% absolute decrease in OEF from the SSS during
hypercapnia relative to baseline scans, demonstrating the utility of MRI susceptibility to
noninvasively image global oxygenation state. However, the technique adopted by Jain et al. did
not spatially resolve OEF measurements to different areas of the brain and did not relate OEF
changes to increases in CBF during hypercapnia.
The primary aim of this study was to test whether our proposed susceptibility-based
approach to measure regional brain oxygenation can detect the expected OEF changes that
accompany the cerebrovascular responses to hypercapnia. We acquired 3-dimensional gradient
echo volumes, each with approximate acquisition time of 6 minutes, during eucapnia and
hypercapnia in young, healthy volunteers at 3 Tesla. After quantitative susceptibility mapping
(QSM) reconstruction of the gradient echo phase data collected during each gas condition
57
(Bilgic et al., 2013b), OEF was estimated locally in individual vessels. These vessels include the
straight sinus and internal cerebral veins draining deep gray matter as well as smaller pial veins
draining various regions of the cortex. Additional quantitative CBF maps were acquired using
established pseudo-continuous arterial spin labeling techniques (Wu et al., 2007). Assuming no
change in CMRO 2 during mild hypercapnia, regional changes in perfusion were used to predict
local oxygenation changes for comparison with our QSM-based OEF measurements in veins
draining different brain regions.
4.3.
Materials and methods
4.3.1.
MRI acquisitions
Imaging data were collected in ten healthy subjects (six males and four females, aged 24 to
31 years) on a Siemens 3 Tesla Tim Trio system with a 32-channel head receive coil. No
subjects had a history of neurological, cardiopulmonary, or psychiatric illness. All subjects gave
written consent under the approval of the local institutional review board. Blood hematocrit (Hct),
the percent of blood consisting of erythrocytes, was measured from a fingerprick sample in each
subject (HemoPoint H2 model #3008-0031-6801, Stanbio Laboratory Inc., Bourne, TX). At the
start of each experiment, we collected a structural magnetization-prepared rapid acquisition
(MEMPR) sequence (van der Kouwe et al., 2008) in the sagittal orientation with repetition time
(TR) = 2530 ms; echo time (TE) = 3.5 ms; inflow time = 900 ms; resolution = 1.0 x 1.0 x 1.0
mm 3; matrix size = 256 x 256 x 176; flip angle (FA) = 90; bandwidth (BW) = 190 Hz/pixel; and R
= 2 parallel imaging acceleration (Griswold et al., 2002) for an acquisition time (TA) of 6
minutes.
Following the anatomical scan, subjects were placed on a breathing apparatus (described
below) and imaged during each of two gas modulation states, eucapnia and hypercapnia.
Gradient echo (GE) phase images were acquired for susceptibility mapping to measure OEF
along brain venous vasculature, and pseudo-continuous arterial spin labeling (pcASL) scans
were acquired to measure perfusion in the deep gray matter and cortical tissue. The order of
MRI acquisitions was counterbalanced between the two conditions to avoid bias due to the
order of the scans.
The 3-dimensional, dual-echo GE scans were flow-compensated along all spatial axes
(Deistung et al., 2009a) with TR = 23 ms; TE = 7.2, 17.7 ms; resolution = 0.875 x 0.875 x 1.0
58
mm 3; matrix size = 256 x 224 x 144; FA = 150; BW = 260 Hz/pixel; partial Fourier (PF) = 6/8; and
R = 2 acceleration for TA of -6 minutes. During the eucapnic state, we also collected three lowresolution, single-echo GE scans with the same spatial coverage to assist with coil combination
of the high-resolution GE data. These scans were acquired with TR = 23 ms; TE = 6-8 ms;
resolution = 1.8 x 1.8 x 2.0 mm 3 ; matrix size = 128 x 112 x 72; FA = 150, BW = 260 Hz/pixel; PF
= 6/8; and R = 2 acceleration for TA of -1 minute per echo. Uncombined magnitude and phase
images were saved for all GE acquisitions.
The 2-dimensional ASL scans were acquired with TR = 3500 ms; TE = 13 ms; resolution =
3.4 x 3.4 x 6.0 mm 3; labeling duration = 1500 ms; post-label delay = 1200 ms; R = 2
acceleration; and 40 control-tag pairs for a total TA of -4 minutes. During each condition, a
separate calibration scan was acquired to map
MOT,
the fully relaxed longitudinal magnetization
of local tissue, for calibration of quantitative CBF values. The parameters of the calibration scan
included TR = 8000; TE = 13 ms; resolution = 3.4 x 3.4 x 6.0 mm 3 ; labeling duration = 1500 ms;
post-label delay = 5000 ms; R = 2 acceleration; and 4 averages for TA of -1
4.3.2.
minute.
Gas manipulations
Resting baseline end-tidal C02 was measured via nasal cannula during the MEMPR scan
with a capnograph (Capstar-100, CWE Inc., Ardmore, PA). After the MEMPR scan, subjects
subsequently breathed through a mouthpiece attached to a specialized breathing circuit similar
to that described by (Banzett et al., 2000). The subject's nose was sealed with medical tape to
ensure that all ventilation was delivered via the breathing circuit. The circuit comprised the
mouthpiece, with an integrated ETCO 2 sample port, connected in series to a heat moisture
exchanger (AirLife@ HEPA Filter, CareFusion, San Diego, CA); a pneuomtach to measure
airway flow and compute tidal volume (ADInstruments MTL300, Colorado Springs, CO);
followed by a Y-shaped non-rebreathing valve (Hans Rudolph, Inc. #2630, Shawnee, KS). The
non-rebreathing valve was configured to permit a limited supply of investigator-controlled gas
through one inlet (controlled gas limb) and allow re-inspired alveolar gas through the other inlet
(alveolar gas limb) when ventilation exceeded the investigator-controlled gas flow. The circuit
maintains end-tidal gases (P0
2
and PC0 2) within ±2 mmHg of target values despite changes in
ventilation (Banzett et al., 2000; McKay et al., 2003).
59
50
50
40E
30&
E 40
E
~
_______
~
~
~
~
~
~
' I
20
_______________
10
30
0
10
-
ETCO
2
20
30
40
50
Minute ventilation
60
time (min)
Figure 4.9. Physiological time courses of end-tidal CO 2 (ETCO 2 ) in mmHg and minute ventilation in
L/min for one healthy volunteer. Green regions indicate transition periods (-4 minutes) between
eucapnia and hypercapnia, and blue regions indicate periods of stable hypercapnia. As expected,
ETCO 2 increased during hypercapnia and was associated with an increase in minute ventilation.
Gases were delivered from tanks containing medical air, 7% carbon dioxide with balance
medical air, and 100% oxygen (Airgas Inc., Radnor, PA). Gas flows were adjusted via a digital
flow-meter (Sierra Instruments, FloBoxTM 954, L Monterey, CA) to achieve the desired gas
mixtures. Subjects received 30% 02 and 70% N2 during the eucapnic condition, and this mixture
was adjusted during the hypercapnic condition (5 - 6.5% CO 2) to target an 8-mmHg increase in
ETCO 2 relative to baseline in each subject.
While on the breathing circuit, ETCO 2 was monitored continuously in each subject via
capnograph
(Capstar-100)
and the airway flow signal
(ADInstruments, Spirometer FE141,
monitored
by a
spirometer
Colorado Springs, CO). Physiological signals were
recorded to computer disk with an analog-to-digital data acquisition system (Powerlab 16/30,
ADlnstruments, Colorado Springs, CO). The ETCO 2 curves were calibrated by accounting for
the expired partial pressure of water vapor (47 mmHg) (Severinghaus, 1989) and barometric
pressure on the day of the experiment. Figure 4.9 depicts representative time courses of ETCO 2
and minute ventilation throughout the experiment from one subject.
4.3.3.
Quantitative susceptibility mapping reconstruction
Phase images were combined from individual channel magnitude and phase volumes after
correcting for spatial RF offsets in each receive channel, as described in (Fan et al., 2013). After
coil combination, all phase images were spatially unwrapped in 3D by the FSL Prelude software
(Jenkinson, 2003), constrained by a brain mask defined from the GE magnitude images with the
60
FSL Brain Extraction Tool (Smith, 2002a). Unreliable voxels with non-linear phase evolution
across TE were identified and were not considered in later processing steps (Schweser et al.,
2011a). Phase image processing was performed independently for images acquired during
eucapnia and hypercapnia.
For each gas condition, a field map estimate (in ppm) was calculated as b =
(PTE2
/
(y -TE2
-BO). Global background fields were estimated by 100 iterations of the projection onto dipole
fields (PDF) routine (Liu et al., 2011a) and subsequently removed, resulting in a local field map
for input into the QSM algorithm. To recover the underlying susceptibility, we adopted a rapid
QSM approach which regularizes the reconstruction with a Total Variation (TV) penalty (Bilgic et
al., 2013b). This regularization improves the quality of the QSM result by incorporating prior
information about the susceptibility distribution we sought to reconstruct. Here, the TV
regularization term promotes a sparse number of nonzero spatial gradients in Y, such that the
optimal X is favored to be piecewise constant within anatomical tissue boundaries. Because the
reconstruction approach is relatively fast, the optimal weighting parameter
(XTv)
for the TV
regularization term was automatically selected for each gas condition separately. This optimal
weighting ranged from 7x10-4 to 9x10-4, and typically did not differ between eucapnic and
hypercapnic scans.
XTV
4.3.4.
Processing of arterial spin labeling scans
The pcASL time series were visually inspected to confirm the absence of severe motion
artifacts (: 3.5 mm inter-scan movement) and realigned in SPM8 to correct for subtle subject
movement (Friston et al., 2007). For each gas condition, quantitative CBF maps in ml/100g/min
were generated from the average difference signal (AM) between control and tag images after
simple subtraction, through the following relationship (Buxton et al., 1998):
CBF =
6000 -A-AM - e PLD/T1,blood
'TIblood
(1 - e
[Eq. 5]
2 -a -T1,blood - MOT
where 2 = 0.9 ml/g is the blood-brain partition coefficient (Herscovitch and Raichle, 1985); PLD
= 1200 ms is the post-label delay of the tag; T1,blood = 1650 ms is the relaxation constant of
arterial blood at 3 Tesla (Lu et al., 2004); a = 0.85 is the labeling efficiency for pcASL
acquisitions (Dai et al., 2008); MOT is the longitudinal magnetization of tissue from the calibration
= 1500ms is labeling duration of the scan. For CBF quantification, PLD was
adjusted per imaging slice to account for the slice acquisition time of 37.5 ms.
scan; and
T
61
Figure 4.10. (a) Minimum intensity projection of gradient echo magnitude images and (b) maximum
intensity projections of quantitative susceptibility maps (ppm) over 20-mm corresponding to eucapnia
and hypercapnia in one volunteer. Notice the diminished vessel contrast due to decreased venous
blood susceptibility during the hypercapnic condition relative to the eucapnic condition on both
magnitude and susceptibility images. Yellow arrows indicate individual vessels of interest including
(1) the straight sinus, (2) the internal cerebral veins, (3) occipital pial veins, (4) parietal pial veins,
and (5) frontal pial veins.
4.3.5.
Quantitative measurements of brain physiology
To guide local physiological measurements, Freesurfer (http://surfer.nmr.mgh.harvard.edu)
was used to reconstruct cortical parcellations from the anatomical MEMPR data in each subject
(Dale et al., 1999; Fischl et al., 1999). The cortical segmentation was registered to the QSM
maps and to the CBF maps with nearest-neighbor interpolation within SPM8 (Friston et al.,
2007).
62
Figure 4.11. Regions of interest (ROI) defined from
cortical
(http://surfer.nmr.mqh.harvard.edu)
Freesurfer
flow
blood
cerebral
segmentation for quantification of local
(CBF) on arterial spin labeling data in one healthy subject.
The regions correspond to the (1) deep gray matter, (2)
thalamus, (3) occipital cortex, (4) parietal cortex, and (5)
frontal cortex. The thalami (2) were also included in
quantification of CBF in the deep gray matter.
For each gas state, venous blood susceptibility was measured in the straight sinus, internal
cerebral veins, and in pial vessels draining the occipital, parietal, and frontal lobes (Figure 4.10).
To avoid potential partial volume effects, only voxels in the highest
2 0 th
percentile of
susceptibility values were considered in the analysis of each vessel for each gas condition.
Across subjects, an average of 7.9 ± 3 voxels per vessel were identified for pial veins, 18.1
+
8
voxels for the internal cerebral veins, and 34.6 ± 9 for the straight sinus. Venous susceptibility
was referenced to that of cerebrospinal fluid (CSF), as estimated from a region of interest (ROI)
with volume 140 ± 39 mm 3 in the anterior portion of the ventricles. The susceptibility difference
between each vein and CSF
(AXvein-CSF)
was then calibrated to quantitative, local OEF by the
same relationship as in Chapter 3 (Haacke et al., 1997b; Weisskoff and Kiihne, 1992a):
AXvein-water
-
AXao - Hct
+ AXoxy-water - OEF - Hct
[Eq. 6]
where AXdo = 0.27 ppm is the susceptibility shift per unit hematocrit between fully oxygenated
and fully deoxygenated erythrocytes (Jain et al., 2012a); A~oxy-water = -0.03 ppm is the
susceptibility shift between oxygenated blood cells and water (Weisskoff and Kiihne, 1992a);
and Hct was measured in each subject.
Regional CBF values during eucapnia and during hypercapnia were determined from ROls
in the deep gray matter, thalamus, occipital cortex, parietal cortex, and frontal cortex, as
depicted in Figure 4.11. Only voxels with greater than 50% probability of assignment to gray
63
matter, as determined from the Freesurfer segmentation (mris-calabel function), were included
in CBF measurements.
4.3.6.
OEF predictions from blood flow changes
Local CBF values were used to predict the expected OEF change in vessels draining each
brain region. Assuming that CMRO 2 is constant in each gas state, and applying Fick's principle
of arteriovenous difference,
CMRO 2 ,H = CMRO 2 ,E
[Eq. 7]
CBFH - OEFH = CBFE - OEFE
[Eq. 8]
OEFH
[Eq.9]
-
-FEOEFE
where the subscript "H" indicates hypercapnia and the subscript "E" indicates eucapnia. If
regional CBF values for a given brain area are known, the predicted normalized change in local
OEF is:
IAOEF|
OEFE
_OEFH-OEFE
OEFE
(CBFH)
(CBFE
1
[Eq. 10]
Note that the OEF predictions are made solely from the ASL measurements of CBF, and are
evaluated against measured OEF changes from independent susceptibility acquisitions.
4.3.7.
Statistical methods
Quantitative OEF and CBF were compared between eucapnic and hypercapnic conditions
using a paired student t-test across subjects. We used Pearson correlation coefficients to test
whether normalized changes in local OEF related to AETCO 2 during the gas challenge. To
compare predicted versus normalized changes in local OEF, we used the Pearson correlation
and also created a T-statistic (T = measured slope / standard error of the slope) to test whether
the slope was statistically different from 1.
64
4.4.
Results
4.4.1.
Measurements of CBF and OEF during eucapnia and hypercapnia
Across subjects, the mean and standard deviation of hematocrit values was 41.6 ± 4%.
Inhalation of CO 2 increased the subjects' ETCO 2 from 41.3
3 mmHg to 49.9 ± 4 mmHg and
increased minute ventilation from 9.9 ± 2 L/min to 24.0
6 L/min. The breathing circuit
facilitated stable levels of ETCO 2 during each gas condition (Figure 1). From the gradient echo
acquisition, diminished contrast in veins, consistent with decreased susceptibility of venous
blood, was observed during hypercapnia relative to eucapnia, both on magnitude and QSM
images (Figure 2).
Quantitative CBF and OEF values for selected a priori brain regions, averaged across
subjects (N = 10), are reported for each gas condition in Table 1. As expected, robust increases
in CBF were observed during hypercapnia relative to eucapnia (P-values < 0.001); with 44.2 +
19% relative CBF increase in deep gray matter, and increases of 48.8% to 64.0% in CBF across
cortical ROls. Large decreases in vessel OEF were also observed during hypercapnia relative
to eucapnia (P-values < 0.001). Specifically, a -42.3 ± 15% relative OEF decrease was
measured from the straight sinus, and approximately -50% decreases in OEF were observed in
pial vessels draining the different cortical regions.
Linear correlations across subjects were observed between percent AOEF and AETCO 2
for each brain regions included in the analysis (Figure 4.12); all P-values < 0.05. The slope of
this linear relationship was similar across most brain areas and ranged from 6.5% / mmHg to
7.5% / mmHg, except in the parietal cortex, for which the slope was 3.7% / mmHg.
4.4.2.
Comparison of measured versus predicted OEF in various brain regions
The observed % change in OEF from susceptibility acquisitions linearly correlated with
predicted % change in OEF from CBF measurements across subjects. This relationship was
observed both in deep gray matter regions (Figure 4.13a) and in more superficial cortical
regions (Figure 4.13b); all P-values < 0.03. The slope of the linear fit between predicted and
measured % OEF for nearly all brain regions was close to identity. These slope values ranged
between 0.98 and 1.26 except for the parietal cortex (with fitted slope of 0.55); although none of
the slopes were statistically different from 1 within our sample.
65
Table 4.2. Mean and standard deviation of cortical physiological parameters measured by
MRI in each gas condition (N= 10).
Region
Eucapnia
Hypercapnia
Relative Change (%)
Cerebral Blood Flow - CBF (mil1 00g/hin)
Deep gray
47.4 ± 5
68.3 ± 7
44.2 ± 19 *
Thalamus
48.9 ± 5
73.3 ± 8
49.9 ± 17
*
Occipital
52.5 ± 3
86.1 ± 12
64.0 ± 17
*
Parietal
51.5± 6
80.4 ±11
56.3 ± 18
*
Frontal
57.8± 6
86.0 ±11
48.8 ± 17
*
Oxygen Extraction Fraction (%)
Straight sinus
26.2 ± 5
14.6
Internal cerebral veins
30.1 ± 6
18.1
Occipital pial veins
27.5 ± 3
12.5
Parietal pial veins
31.6 ± 4
14.0
-55.7
7
Frontal pial veins
28.5 ± 4
13.6
-52.3
13 *
-42.2 ± 15 *
+
*
-39.9 ± 14 *
+
-54.6 ± 12
*
*
paired t-test P-value < 0.001
66
A
Straight sinus
internal cerebral veins
100% ,
80% I-.
0
U
'LI
60%
100%
y = 6.5x - 11.4
R = 0.58
y = 6.6x - 14.0
R = 0.60
80%
6
-
60%
LL
'hi
0 40%
40% -
0
IL
20% 0%
0 20%
,
.
.
-
0
5
10
15
0%
-
0
5
AETCO 2 (mmHg)
B
Occipital pial veins
100% 1
80%
-
60%
-
15
Parietal pial veins
100% -
y = 6.7x - 2.0
R = 0.67
y = 3.7x + 25.4
R = 0.64
80%
*
L 60%
Le
0
10
AETCO 2 (mmHg)
U-
40% -
0
20% -
o20%
.. 40%
IL
0
0%
0%
0
5
10
15
0
5
10
15
AETCO 2 (mmHg)
AETCO 2 (mmHg)
Frontal pial veins
100% '1
y = 7.5x - 9.2
R = 0.69
80%
LL
60%
LU
9
40%
U-
0
<
20%
0%
0
5
10
15
AETCO 2 (mmHg)
Figure 4.12. Scatter plots across subjects of normalized % change in oxygen extraction fraction
(OEF) versus increase in end-tidal C02 (ETCO 2) in mmHg. The plots are generated separately for
(a) the straight sinus and internal cerebral veins draining deep gray matter; and (b) cortical pial
vessels draining surface gray matter. Linear fits are shown for each graph with slope (% OEF /
mmHg) indicating reactivity of vessel OEF to the hypercapnic challenge, and R value to characterize
the goodness of fit.
67
Straight
,
80%
Internal cerebral veins
sinus
80% -1
UL
U-
LU
0
6ft60%
*%
0
60%
-
-
b
b
0
VU 40% -
0 40%.
20%.
20%-
y = 1.26x + 0.06
R = 0.78
0%
0%
40%
20%
60%
y = 0.98x + 0.08
R = 0.62
0%
0%
80%
60%
80%
80%
,
60%
-
I"
LuA
0
0
60%
w
U.
b
V
M
40%
Parietal piai veins
occipital pial veins
80% -
VU
U'R
20%
Predicted % decrease OEF
(from CBF)
Predicted % decrease OEF
(from CBF)
40% -
'U
U,
V
I' '20%-
0%
20%
y = 0.65x + 0.37
R = 0.63
y = 1.23x + 0.08
R = 0.66
L
0%
0%
80%
Predicted % decrease OEF
(from CBF)
40%
20%
60%
0%
20%
40%
60%
Predicted % decrease
(from CBF)
80%
OEF
Frontal pial veins
80%
ILL
0
60%
b
VI
+
40%
20%
y 1.16x + 0.15
R = 0.72
0%
0%
20%
.
.
:
40%
60%
80%
Predicted % decrease OEF
(from CBF)
Figure 4.13. Scatter plots across subjects of measured versus predicted percent change of oxygen
extraction fraction (OEF) in (a) deep gray matter, and in (b) superficial cortical regions. Measured
OEF values (vertical axis) derive from susceptibility measurements in individual veins; while the OEF
predictions (horizontal axis) are determined solely by cerebral blood flow (CBF) values from the
arterial spin labeling acquisitions. Linear fits are shown for each graph with slope with R value to
characterize the goodness of fit.
68
4.5.
Discussion
The present study demonstrates the first MRI-based non-invasive approach to provide
reliable measures of regional brain oxygen utilization. Susceptibility measurements from flowcompensated
GE scans revealed
large decreases in OEF
during hypercapnia. We
demonstrated robust OEF reductions in five brain areas, including three distinct cortical regions
delineated on structural scans, which related to independent perfusion metrics from established
pcASL imaging techniques.
4.5.1.
Vessel contrast during gas modulation
Consistent with previous hypercapnia studies (Gauthier et al., 2011; Rauscher et al.,
2005a; Sedlacik et al., 2008), we observed loss of venous contrast on the GE magnitude
images during hypercapnia relative to baseline. This change in contrast was driven by a
decrease in dHb content, and thus a decrease in y within brain vessels, which was also
observed on the QSM maps reconstructed from the GE phase images. Importantly, the QSM
maps are quantitative and self-referenced to x values in the CSF, such that the vessel signal
intensity is not influenced by T1 relaxation or by field inhomogeneity profiles. These factors may
allow for changes in venous contrast to be more apparent on QSM images (Figure 1b)
compared to magnitude images (Figure 1a).
4.5.2.
Regional OEF changes during hypercapnia
Similar to previous work, we measured strong reductions of local venous OEF during
hypercapnia relative to baseline within individual vessels. For instance, OEF decreased by
-40% in the straight sinus and in the internal cerebral veins draining the deep gray matter; and
decreased by over 50% in pial veins draining various cortical territories. The amplitude of our
measured OEF changes are consistent with the 42.6% OEF decrease in the SSS observed by
TRUST MRI during inhalation of a similar 5% CO2 gas mixture (Xu et al., 2011). However, these
changes are also greater than the 18.4% OEF reduction in the straight sinus (Sedlacik et al.,
2008) and 20% OEF reduction in the SSS (Jain et al., 2011b) previously reported from GE
magnitude and phase images, respectively. Although the hypercapnic challenges delivered in
each study are comparable, each investigation applied a distinct physical contrast mechanism
and image processing stream to measure oxygenation. As such, baseline absolute OEF varied
69
from 24% to 36% across reports, and contributes to differences in observed OEF changes
across studies.
Notably, although we targeted the same ETCO 2 elevation in each individual, subjects
exhibited slightly different ETCO 2 responses to the respiratory challenge. The OEF reductions in
each brain region correlated across subjects with the achieved increase in ETCO 2 (Figure 4).
The slope of these relationships characterizes the cerebrovascular reactivity (CVR) of vessels,
and we observed a 6.2 ± 1% change in OEF per mmHg increase of ETCO 2 across the brain. If
oxygen metabolism is constant in both gas states, the magnitude of the OEF response is
expected to be similar to the perfusion response (per mmHg increase in ETCO 2) during
hypercapnia. In fact, the CVR computed from our susceptibility-based OEF values is in line with
CVR computed from MRI flow in previous hypercapnia studies. For instance, Jain et al.
observed a CVR of 6.1% / mmHg from phase-contrast flow measurements in major cerebral
arteries (Jain et al., 2011 b); although other studies have reported slightly lower values for CVR
(-3% / mmHg) from ASL perfusion measurements in the gray matter during hypercapnia (Heijtel
et al., 2014; Villien et al., 2013).
As an independent measure of the hemodynamic response to hypercapnia, pcASL
perfusion maps were also collected in each gas state. Regional CBF values were used to
predict the OEF change expected in brain vessels associated with each brain area, assuming
no change in CMRO 2. OEF reductions during hypercapnia correlated with predicted OEF
changes in all brain regions investigated for this study. This finding suggests our method
provides physiologically meaningful, local OEF estimates in various cerebral veins that are
coupled to regional flow. Here we adopted a pseudo-continuous labeling scheme for flow
imaging similar to those prescribed by past studies (Chen et al., 2011; Xu et al., 2010) and
recent consensus among the field (Alsop et al., 2014). At the same time, we acknowledge that
ASL techniques are still under development and potential quantification errors - e.g. due to low
labeling efficiency (underestimation of CBF) or hyperintense intravascular artifacts from arteries
(overestimation of CBF) - could increase the variance on OEF predictions in this study.
4.5.3.
Assumption of constant CMRO 2 during hypercapnia and potential pitfalls
An important assumption for OEF predictions in the current study is that CMRO 2 remains
unchanged between eucapnia and mild hypercapnia. This assumption is supported by invasive
studies (Kety and Schmidt, 1948) and imaging studies in healthy adults. These imaging studies
70
combined oxygenation estimates in the SSS (Jain et al., 2011b) or in the jugular veins of the
neck (Chen and Pike, 2010) with flow measurements, and found no change in global CMRO 2
during mild hypercapnia. However, one recent MRI study found that breathing CO 2 led to 13%
suppression of global CMRO 2 (Xu et al., 2011). In the context of this work, a small decrease in
CMRO 2 could be driven by a greater OEF reduction than predicted from underlying CBF
increases. There may have been evidence of these effects in the present study, as seen in the
plot of measured versus predicted OEF changes (Figure 5), in cases for which the slope was
greater than 1. However, this trend was not observed consistently across brain regions, at least
within the measurement error of our experiments.
One limitation of this study is the relatively coarse pairing between vessels (identified on
the GE images) and the brain regions corresponding to their drainage (delineated by structural
ROls overlaid on the CBF maps). Venous blood drainage from the brain is less stereotyped than
the arterial system, and there are no validated methods to define the drainage pattern for
cerebral veins, especially on the scale of the pial vessels identified here (-2mm in diameter).
Furthermore, although hypercapnia is generally considered to affect the brain in a global
manner, there is evidence for regional variations in the cerebral hemodynamic response to this
gas challenge. In one study, the BOLD signal change to hypercapnia was higher in the
cerebellum and visual cortex than in the frontal cortex (Kastrup et al., 1999b). If the vascular
response to hypercapnia follows a predictable spatial pattern across the brain, careful
identification of the drainage territory would be necessary to accurately predict the local OEF
change in each vessel. Consequently, our analysis would benefit from a noninvasive technique
to image the drainage of tissue to cerebral vessels (Wong and Guo, 2013b).
4.5.4.
Future methodological improvements to oxygenation imaging
For this investigation, we focused on normalized OEF changes (OEF
OEFE
measured by
MRI susceptibility during a respiratory challenge. This OEF metric was selected because of its
robustness to absolute quantification errors, which may occur due to partial volume effects and
flow-induced phase artifacts that persist despite flow compensation in the GE acquisitions.
These secondary flow artifacts occur due to moving blood spins through an inhomogeneous
susceptibility field gradient, and can lead to nearly 30% overestimation in absolute OEF if
uncorrected (Xu et al., 2013). For instance, the SSS is a large draining vessel with a relatively
fast flow of 280 mL/min (Jordan et al., 1994). Its geometry and orientation with respect to the
71
magnetic field gradient is also more complex than the straight sinus. For these reasons, the
SSS did not appear uniform in contrast as expected in the 3-dimensional QSM maps, and it was
challenging to reliably measure OEF in the SSS from susceptibility images.
In the absence of quantification biases, absolute CMRO 2 could theoretically be
computed via the Fick principle from OEF and CBF values for each gas condition (Fan et al.,
2012; Kety and Schmidt, 1948). With our raw OEF measurements, CMRO 2 would decrease by
30%, from 121 ± 15 pmol/100g/min during eucapnia to 86 ± 15 ptmol/100g/min during
hypercapnia across subjects. This CMRO 2 reduction is much greater than previous imaging
reports. After correction for 30% overestimation in OEF due to flow artifacts (Xu et al., 2013),
however, CMRO 2 would only decrease by 10% from 167 ± 17 pmol/100g/min during eucapnia
to 150.8 ± 15 pmol/100g/min during hypercapnia. This reduction is not significant and is more in
line with the literature (Chen and Pike, 2010; Jain et al., 2012a). We note that the corrected
values for absolute CMRO 2 are also more consistent with 150 PET measurements in the cortex
(lbaraki et al., 2008; Ishii et al., 1996). This analysis suggests that secondary flow effects may
contaminate the absolute quantification of OEF, and thus of CMRO 2, in smaller pial vessels.
Future improvements to the method could directly model and remove flow artifacts on phase
images to accurately calibrate absolute values of OEF and CMRO 2.
To achieve sufficient resolution for local OEF measurement from small vessels, our GE
scans required longer acquisition time (-6min) in each gas state, throughout which the cerebral
physiology of interest presumably remained stable. This acquisition is many times longer than
needed for global oxygenation measurements such as TRUST MRI (1:12 minutes) (Xu et al.,
2011) or phase measurements in the SSS (22 seconds) (Jain et al., 2011b). The long
acquisition time limits the number of echoes that can be collected in one scan, especially since
long flow compensation waveforms between echoes are necessary for reliable x estimation in
vessels. As such, our method would benefit from more efficient sampling trajectories, such as
spirals (Wu et al., 2012a), to collect GE phase images at multiple (more than three) echoes in a
reasonable scan time. A multi-echo, fast acquisition would improve the accuracy of y estimates,
enable the modeling and removal of flow artifacts on phase images, and facilitate application of
the oxygenation measurement technique in clinical settings.
72
4.5.5.
Conclusions
This study demonstrates the ability of quantitative susceptibility MRI to noninvasively
measure regional changes in brain OEF during cerebrovascular challenge induced by
hypercapnia. Robust OEF changes in individual veins were observed during hypercapnia
relative to eucapnia. Furthermore, the measured OEF changes in each brain area correlated
with predicted changes made from independent perfusion scans, providing confidence in the
fidelity of oxygenation measures from QSM reconstructions. MR susceptibility imaging of OEF is
a reliable, quantitative technique that can contribute to clinical management of brain disorders
such as stroke and tumor, and improve our understanding the pathophysiology of
neurodegenerative diseases.
73
74
5. Application: Quantitative oxygenation extraction
fraction and reproducibility in multiple sclerosis at 7
Tesla MRI susceptibility
5.1.
Abstract
Contribution: The aim of this chapter was to apply a beta version of susceptibility imaging of
oxygenation in a patient population. We used quantitative susceptibility at 7 Tesla (7 T) MRI to
estimate oxygen extraction fraction (OEF) in cortical veins from patients with multiple sclerosis
(MS) and healthy subjects in various brain regions, and assessed OEF reproducibility.
Written consent for the study was obtained from 23 patients with MS and 14 age-matched
controls under approval from the local Institutional Review Board. We collected 3D flowcompensated fast low-angle shot (FLASH) GE scans for OEF quantification and T2*-weighted
images for characterization of white matter (WM), deep gray matter (GM), and cortical lesion
types. In patients, we assessed processing speed, executive function, and learning-recall
abilities. Variability of OEF across readers and scan sessions were assessed in a subset of
volunteers. OEF was averaged from 2-3 pial veins in the sensorimotor, parietal, and prefrontal
cortical regions for each subject (total of -10 vessels), and compared between patients and
healthy controls with repeated-measures analysis of variance. OEF were related to MRI
structural changes and with cognitive scores by Spearmanrho analyses.
The intra-observer coefficient of variation (COV) = 2.1%, inter-observer COV = 5.2%, and
scan-rescan COV = 5.9% for OEF reproducibility analyses. Patients exhibited a 3.4% cortical
decrease in OEF relative to controls (P = 0.0025) which was not different across brain regions.
Although oxygenation was not associated with any measures of structural tissue damage, mean
cortical OEF correlated with processing speed and executive function Z-scores. We found that
cortical OEF from 7 T MRI susceptibility is a reproducible metabolic biomarker that potentially
provides complementary information to structural MRI on disease status in patients with MS.
75
5.2.
Introduction
One promising new MRI approach quantifies oxygenation levels on gradient echo phase
images. Because deoxyhemoglobin molecules in veins are paramagnetic, the susceptibility shift
between a vessel and its neighboring brain tissue creates oxygenation-dependent field
perturbations that manifest on MRI phase images (Haacke et al., 1997a; Weisskoff and Kiihne,
1992b). Through modeling each vessel as a long cylinder parallel to the main magnetic field,
there is a simple relationship between the observed phase difference between venous blood
and brain tissue to the underlying oxygen extraction fraction (OEF) in the vein. This approach
has been demonstrated in large draining vessels of the brain (Jain et al., 2010) and femoral
vessels of the knee (Fernandez-Seara et al., 2006).
Because OEF quantification is made in individual veins, the phase-based method innately
offers regional metabolic information given sufficient spatial resolution of the vessels (Fan et al.,
2012). Furthermore,
these measurements can take advantage of improved resolution
achievable with high signal-to-noise (SNR) images acquired at ultra-high field (7 Tesla; 7 T and
higher) to probe smaller veins that are more indicative of local brain function (Vaughan et al.,
2001). This technique has not been attempted at 7 T and there is little understanding of the
variability in oxygenation values across scan sessions and readings.
As a potential clinical application of the MRI method, there is recent evidence that metabolic
disturbance in multiple sclerosis (MS) could relate to lesion formation and inflammation near
cortical veins (Kidd et al., 1999) as well as cognitive decline in patients. Cortical damage is
thought to play a major role in determining physical and cognitive disability in multiple sclerosis
(MS), and the type and location of lesions in the cortex are closely related to the size and
territory of involved cortical veins (Adams et al., 1989). A perivenous origin has also been
shown for white matter (WM) lesions (Fog, 1965; Tallantyre et al., 2008; Tan et al., 2000).
Characterization of venous oyxgenation in the cortex would help elucidate metabolic changes
associated with lesion formation and energy failure in the disease, which could interfere with
axonal function and contribute to clinical disability and cognitive decline.
Initial studies on brain oxygen consumption in MS performed using 150 PET demonstrated
reductions of absolute cerebral blood flow (CBF) and cerebral oxygen metabolism (CMRO 2) in
both gray matter (GM) and WM (Brooks et al., 1984; Sun et al., 1998). Similarly, MRI
observations of decreased R2' relaxation rate in frontal normal-appearing WM (Holst et al.,
2009) and diminished visibility of periventricular veins on susceptibility-weighted images (Ge et
76
al., 2009) indicate decreased oxygen extraction in MS. In a previous study, Ge et al. applied T2Relaxation-Under-Spin-Tagging (TRUST) MRI to directly measure absolute OEF in the sagittal
sinus of patients with relapsing-remitting (RR) MS for the first time with MRI (Ge et al., 2012; Lu
and Ge, 2008). Ge et al. found reductions in global OEF in patients relative to healthy controls,
which correlated with clinical disability and lesion volume.
The first aim of this study was thus to implement phase-based oxygenation measurements
at high field MRI. At 7 T, we achieved sub-millimeter resolution in gradient echo phase images
to estimate OEF in small vessels, and assessed the intra-observer, inter-observer, and scanrescan reproducibility of the measurements. With this MRI susceptibility approach, we
compared OEF in separate cortical regions in patients at different stages of MS relative to agematched healthy controls. Finally, because the pathological and clinical correlates of altered
oxygen metabolism are not well understood, we performed an exploratory analysis to evaluate
quantitative OEF changes against MRI measures of tissue damage and measures of clinical
disability in patients.
5.3.
Materials and Methods
5.3.1. Patients and Control Subjects
Thirty-seven subjects including 23 patients (17 women; mean age ± SD = 41.8 ± 7 years)
and 14 age-matched controls (8 women; mean age ± SD = 39.5 ± 7) were recruited for this
prospective study. Demographic and clinical information, including Extended Disability Status
Scale (EDSS) values (Kurtzke, 1983), for the patients are provided in Table 5.3. Eligibility
criteria for patients were: age between 18-64 years, a diagnosis of clinically isolated syndrome
(CIS) or clinically definite MS (Polman et al., 2011), absence of a clinical relapse within three
months, and no corticosteroid therapy within one month of study initiation. RPK and ASN,
board-certified and MS neurologists, recruited patients presenting with CIS/early MS (N = 6);
relapsing-remitting (RR) MS (disease duration of greater than 3 years) (N = 11); and secondaryprogressive (SP) MS (N = 6); according to accepted disease phenotype criteria for clinical and
research purposes (Polman et al., 2011).
77
Table 5.3. Demographics and cognitive characteristics of 23 patients with multiple
sclerosis
Characteristic
Mean (range)
Patients
(N=23; 17 females)
Disease duration (years)
8.5 (0.42 - 22)
-0.48
0.02
EDSS Score
3.0 (0 -8)
-0.42
0.04
Processing Speed
(z-score)
-0.01 (-1.03 - 0.76)
0.56
0.01
Executive Function
(z-score)
-0.47 (-3.48 - 0.82)
0.48
0.03
Learning-Recall Ability
(z-score)
-0.19 (-2.62 - 1.91)
0.30
0.17
Global Cognitive Ability
(z-score)
-0.10 (-2.37 - 1.04)
0.36
0.10
Spearman
mcorrelatical
EF
Uncorrected
P-value
EDSS = Expanded Disability Status Scale
Exclusion criteria for this study included significant psychiatric and/or neurological disease
other than MS, medical comorbidity, or a condition that could affect OEF measurements
including cardiovascular disease, respiratory syndrome, or anemia. Seventeen patients were on
immunomodulating treatments and 5 were untreated at the time of the MRI. The study was
performed with informed consent from each subject under approval from the local Institutional
Review Board. The patients in this analysis are a subset of the 34 patients previously reported
in (Nielsen et al., 2013). The prior article evaluated the contribution of focal cortical lesion
subtypes at ultra-high-field and traditional MRI metrics on cognition, whereas this manuscript
assessed cortical oxygenation in patients relative to healthy volunteers.
5.3.2. Data Acquisition
Subjects were first scanned on a 7 T MRI (Siemens, Erlangen, Germany) with a 32-channel
phased-array coil developed in-house. Five controls were scanned twice in sessions scheduled
a week apart, with repositioning based on localizers (Gallagher et al., 1997). For OEF
measurements, axial 3-dimensional (3D) fast low-angle shot (FLASH) gradient-echo images
were acquired with magnitude and phase. Scan parameters included repetition time [TR]=26ms;
78
echo time (TE) = 6-6.4,10 ms; in-plane resolution = 330x330 pm 2 ; field of view = 168x192x64
mm 3 ; slice thickness (TH) = 1 mm; flip angle (FA) = 150; bandwidth (BW) = 130 Hz/pixel;
GRAPPA acceleration = 2 (Griswold et al., 2002).
Sixty-four slices were acquired either as two slabs (6 patients and 1 control) or one slab
(remaining participants) to cover the supratentorial brain. Separate scans with flow
compensation in all directions were collected for each TE, with total acquisition time [TA] - 16
min. High-resolution anatomical scans for characterizing WM lesion volume (LV), deep GM LV,
and cortical lesion counts (LC) included 2-dimensional (2D) FLASH T2*-weighted spoiled
gradient-echo images (TR/TE = 1000/22 ms, 2 slabs, in-plane resolution = 330x330 pm 2 , TH =
1 mm, 0.25-mm slice gap, TA per slab -7 min).
Within a week of the 7 T scan, all patients and 12 control subjects also underwent
anatomical scanning at 3 T with a multiple-echo, magnetization-prepared rapid acquisition
(MEMPR) sequence (van der Kouwe et al., 2008) for tissue segmentation using Freesurfer
(http://surfer.nmr.mch.harvard.edu/) (Dale et al., 1999). The structural scans were acquired with
TR = 2530 ms, inflow time=1200 ms, TE=1.7/3.6/5.4/7.3 ms, FA=7", resolution = 0.9x0.9x0.9
mm 3 , BW = 651 Hz/pixel.
5.3.3.
Data Processing for OEF Quantification
The FLASH phase images were high-pass filtered for each slice (96x96 Hanning) to mitigate
background field inhomogeneities and remove phase wraps (Wang et al., 2000b). Because
vessels of interest were spatially narrow (1-4 pixel diameter), the high-pass filter is expected to
preserve the relevant phase shift between the vein and surrounding tissue while removing
global phase variations (Fan et al., 2012).
For each subject, using the Desikan atlas from Freesurfer (Desikan et al., 2006), OEF was
quantified for 2-3 veins in each of the sensorimotor (precentral and postcentral regions),
parietal, and prefrontal regions of the cortex and averaged to obtain a measure of mean cortical
OEF. Candidate pial veins were only included in the analysis if the tilt angle was less than 200
relative to the main magnetic field (Bo) and diameter was less than 2 mm. The selected vessels
were modeled as long cylinders parallel to BO to quantify the susceptibility difference between
the vessel and parenchyma (Fan et al., 2012; Haacke et al., 1997a; Jain et al., 2010), as
depicted in Figure 3.1. Examples of the distribution of cortical vessels selected for analysis are
illustrated in Figure 5.15.
79
Figure 5.14. Orientation and geometry of representative cortical vessel segment in a patient with
clinically isolated syndrome. (a) Sagittal views of magnitude (top) and filtered phase (bottom) from
the gradient echo acquisition. The rectangles highlight the vessel identified in (b), which depicts the
zoomed magnitude (left) and phase (right) of the vein. The double-sided arrow indicates the
segment of the vessel approximately parallel to Bo. (c) Axial view of magnitude (left) and phase
(right) of the same vessel, as indicated by the single-sided arrow.
Figure 5.15. Examples of the distribution of cortical vessels in various brain regions selected for
quantitative oxygen extraction measurements. Axial phase images are displayed after filtering from a
control subject (top) and a patient with secondary progressive MS (bottom).
80
After vessel identification, the field shift between the vein and tissue, ABvein-tissue, was
calculated from the phase signal evolution across both echoes. Phase inside the vessel was
averaged from 1-4 bright pixels per slice across 2-3 slices intersecting the vein, while phase
from tissue was estimated from a manually drawn region with volume of 19.5-26.1 mm 3 adjacent
to the vessel. Applying MR susceptometry, local OEF was determined for each parallel vein
through the relationship
ABvein-tissue=
1/6-4T-
AXdO-
Haacke et al., 1997a). Here, the vessel tilt angle
Hct- OEF-(3cos 2O-1)- Bo (Fan et al., 2012;
e was assumed to
be 0, Axd,= 0.27 ppm (Cgs)
was the susceptibility shift per unit hematocrit between fully deoxygenated and fully oxygenated
blood (Jain et al., 2011a), and hematocrit Hct was measured in 16 patients and assumed to be
42% (the mean of the measured hematocrit values) in all remaining subjects.
To evaluate the reproducibility of the OEF values, one observer identified vessels and
measured OEF in 5 patients and 5 controls, then repeated the analysis on the images after one
week. Independently, a second observer quantified OEF from the same dataset. Both observers
used a graphical user interface developed in-house (MATLAB, Natick MA) to facilitate
visualization of vessels and selection of appropriate voxels for OEF measurements. The first
observer also measured OEF for the two scans acquired a week apart in five separate healthy
volunteers. Candidate vessels were selected independently for all OEF readings.
5.3.4. MRI Characterization of Tissue Volumes, Lesion Volumes and Lesion Counts
FreeSurfer tissue segmentations were performed on the root-mean-square average across
the echoes of the 3 T MEMPR scan. To correct for topological defects in the segmentation
caused by hypointense MS lesions, WM and leukocortical lesions were filled on the WM mask in
FreeSurfer. Reconstructed surfaces and subcortical segmentations were subsequently checked
in each subject to ensure accuracy before extraction of cortical thickness and thalamic volumes
for each subject.
Two separate observers, blinded to subject demographic and clinical characteristics and
with over ten years of experience in radiology, independently quantified lesion counts from focal
cortical hyperintensities on 2D FLASH-T 2* magnitude images in patients (Mainero et al., 2009).
Cortical lesions were categorized as Type I (leukocortical) extending across both WM and GM;
Type II (intracortical) within the cortical tissue; or Type III/IV (subpial) extending throughout the
cortical width without entering the WM (Stadelmann et al., 2008). Cortical LCs only included
lesions in agreement between the two observers, and were only assessed in 21 patients due to
81
slight motion artifacts. Deep GM and WM hyperintense lesions were segmented from 2D
FLASH-T 2* magnitude images with a dedicated software (Alice, Hayden Image Processing
Solutions) using a local threshold contouring technique to quantify LVs (Mainero et al., 2009).
5.3.5. Neuropsychological Testing Methods
Within a week of the 7 T session, a licensed neuropsychologist (with over 20 years of
experience) administered neuropsychological testing to all patients, including: the Symbol Digit
Modalities Test (SDMT); Controlled Oral Word Association Test (COWAT); Trail Making Test
(Trails A and B); California Verbal Learning Test-Il (CVLT-ll), Brief Visuospatial Memory TestRevised (BVMT-R); and Wisconsin Card Sorting Test-64 Card Version (WCST-64). The
Wechsler Test of Adult Reading (WTAR) test was administered to assess premorbid
intelligence. Raw test scores were converted to z-scores based on age- (and when available,
education- and gender-) based norms, referenced in Table 5.4.
To assess cognitive domains commonly affected by MS, for each patient we calculated a
score for processing speed (averaging Trails A, SDMT, and COWAT Z-scores), executive
function (averaging WCST-64 [Total Categories], WCST-64 [Total Preservative Responses] and
Trails B Z-scores), and learning-recall ability (averaging BVMT-R [Total Learning], BVMT-R
[Delayed Recall], CVLT-1l [Total Recall Score] and CVLT-ll [Long Delay Free Recall] Z-scores).
A global cognitive score was computed for each patient by averaging scores from the three
cognitive domains.
5.3.6. Statistical analysis
Statistical analysis was performed using the Statistical Analysis System software (SAS Inc.,
Cary, NC). Reproducibility of mean cortical OEF was assessed with coefficients of variation
(COV=SD/mean)
between each couple of OEF readings for intra- and inter-observer
comparisons; or between each couple of acquisitions for scan-rescan comparisons. Across all
patients, associations between mean cortical OEF and MRI indices of cortical thickness,
thalamic volume, and structural tissue damage (LCs and LVs) were explored using Spearman
rho correlation tests, initially without correction for multiple comparisons. Similar correlations
were computed between mean cortical OEF and clinical characteristics in patients. Differences
in OEF across regions and between patients and controls were assessed using a repeated
measures analysis of variance (ANOVA). This comparison was followed by Mann-Whitney tests
82
for each cortical area, with correction for multiple comparisons by controlling for false discovery
rate.
Table 5.4. Table of References for Neuropsychological Test Normative Values
Neuropsychological Test
Symbol Digit Modalities Test
(SDMT)
References
1.
Smith, A. Symbol Digit Modalities Test Manual - Revised.
Los Angeles, CA: Western Psychological Services; 1991.
Controlled Oral Word
Association Test (COWAT)
2. Tombaugh TN, Kozak J, Rees L. Normative data stratified
by age and education for two measures of verbal fluency:
FAS and animal naming. Archives of Clinical
Neuropsychology 1999;14:167-177.
Trail Making Test (Trails A
and B)
3. Tombaugh TN. Trail Making Test A and B: Normative data
stratified by age and education. Archives of Clinical
Neuropsychology 2004; 19:203-214.
California Verbal Learning
Test-Il (CVLT-ll)
4. Delis DC, Kramer JH, Kaplan E, Ober BA. California
Verbal Learning Test, 2nd ed Adult Version Manual. San
Antonio: The Psychological Corporation; 2000.
Brief Visuospatial Memory
Test-Revised (BVMT-R)
5. Benedict RHB. Brief Visuospatial Memory Test - Revised:
Professional Manual. Odessa: Psychological Assessment
Resources; 1997.
Wisconsin Card-Sorting Test
6. Kongs SK, Thompson LL, Iverson GL, Heaton RK.
Wisconsin Card Sorting Test - 64 Card Version
Professional Manual. Lutz: Psychological Assessment
Resources, Inc.; 2000.
- 64 Card Version (WCST-64)
Weschler Test of Adult
Reading (WTAR)
7. Weschler D. Weschler Test of Adult Reading (WTAR)
Manual. San Antonio: The Psychological Corporation;
2001.
Beck Depression Inventory-Il
(BDI-l I)
8. Beck AT, Steer RA, Brown OK. Beck Depression
Inventory, 2nd ed Manual. San Antonio: The Psychological
Corporation; 1996.
83
5.4.
Results
Quantitative results from the reproducibility analysis of mean cortical OEF are presented in
Table 5.5. No significant differences in cortical OEF averaged across subjects were found
between readings made by the same observer, between different observers, or between scan
sessions a week apart. The intra-observer reproducibility of mean OEF was excellent, with COV
= 2.1%; inter-observer and scan-rescan analyses revealed similar low COV's of 5.2% and 5.9%,
respectively. Not surprisingly, the overall percentage of vessels independently re-identified was
higher (60.2%) for intra-observer comparisons, relative to inter-observer (25.9%) and scanrescan (34.1%) comparisons. To visualize the variability in cortical OEF, Bland-Altman plots of
intra- and inter-observer OEF reproducibility for 10 subjects are depicted in Figure 5.16a-b.
Scan-rescan OEF values for 5 separate healthy controls are plotted with confidence intervals
computed from the mean standard deviation across the group (Figure 5.16c).
MRI measures of cortical thickness, thalamic volume, and structural tissue damage across
patients are presented with in Table 5.6. A weak correlation was detected between mean
cortical OEF and cortical thickness (p = 0.41, uncorrected P = 0.05). No relationship was
detected between OEF and MRI measures of structural tissue damage, including LVs and LCs.
Mean cortical OEF was 31.5 ± 3% across all controls (N = 14), and 28.1 ± 3% across all
patients (N = 23). From the repeated measures ANOVA, oxygen extraction was found to be
lower in all patients compared to controls (P = 0.0025), but there was no difference in OEF
across the cortical regions (P = 0.98). Because an association was found between OEF and
cortical thickness, the ANOVA was repeated with cortical thickness as a nuisance factor.
Oxygen extraction remained significantly lower in patients compared to controls (P = 0.03) after
adjusting for cortical thickness. Boxplots of OEF are shown separately in each cortical area and
averaged across the cortex in Figure 5.17. The Mann-Whitney tests revealed decreased OEF in
the sensorimotor (adjusted P = 0.02), parietal (adjusted P = 0.03), and prefrontal cortices
(adjusted P = 0.01).
84
Table 5.5. Mean and standard deviation of OEF across the cortex (%) in reproducibility
analyses.
Intra-observer
Reading #1
Reading #2
P-alue
Veins selected in
Controls (N = 5)
32.8 ± 4
32.5 ± 3
0.71
61.2
Patients (N = 5)
28.2 ± 2
28.8 ± 2
0.95
59.2
Inter-observer
Observer #1
Observer #2
-vtest
Veins selected in
Controls (N = 5)
32.8 ± 4
33.4 ± 2
0.56
33.3
Patients (N = 5)
28.2 ±2
30.1 ±3
0.15
15.0
Scan-rescan
Week #1
Week #2
T-test
P-value
Veins selected in
common(%
Controls (N = 5)
29.3 ± 3
30.2 ± 1
0.50
34.1
Table 5.6. MRI characteristics of 23 patients with multiple sclerosis
Characteristic
Mean (range)
Patients
(N = 23; 17 females)
Cortical thickness
(mm)
3T
2.4 (2.2 - 2.6)
0.41
0.05
Thalamic Volume
3T
13767 (10837-
-0.01
0.98
-0.34
0.12
(mm
Spearman
correlation
mean corticalwith
QEF
Uncorrected
P-value
Scan
17942)
3)
WM Lesion Volume
2232(64 -15718)
(MM 3)
7T
Dee GM Lesion
7T
92 (3
258)
-0.08
0.73
Type I Lesion Count
(N)
7T
5.5(0-42)
-0.17
0.47
Type 11 Lesion Count
7T
0.14 (0
-
-
Type III/IV Lesion
Count (N)
7T
15.4(0-41)
-0.33
0.15
Total Cortical Lesion
Count (N)
7T
21.1 (0-82)
-0.32
0.16
(N)
-
-
2)
WM = white matter; GM = gray matter; 3 T = 3 Tesla; 7 T = 7 Tesla
85
A
B
Intra-observer
10
U-
U-
5
W 5
0
--- - -- --0
0
E)
0
--- - - - - - -
0
- -- - - -
--- - - --
-5
-5
-10 1
20
-10
25
Average
C
Inter-observer
10
30
35
40
OEF for two readings (%)
20
25
30
35
40
Average OEF for two observers (%)
40
35
U-
w1 30
0
25
20
1
2
3
4
5
Subject (Scan-Rescan)
Figure 5.16. Bland-Altman plots depicting (a) inter-observer and (b) inter-observer reproducibility of
mean cortical OEF made from the same data in 5 controls and 5 patients. (c) Scatter plot depicting
scan-rescan variability of mean cortical OEF in 5 separate healthy subjects scanned twice in
sessions a week apart. The diamonds show confidence intervals based on the mean standard
deviation of OEF across sessions computed in the group.
Correlations were observed between mean cortical OEF and patient disease duration (p =
-
0.48, uncorrected P = 0.02) and with EDSS score (p = -0.42, uncorrected P = 0.04). Across all
patients, performance on cognitive evaluations revealed relatively minor cognitive impairment,
with the lowest mean normalized Z-score of -0.47 in executive function (Table 5.3). The
strongest correlations detected for mean cortical OEF were with processing speed (p = 0.56,
uncorrected P = 0.01), and with executive function speed (p = 0.48, uncorrected P = 0.03), as
plotted in Figure 5.18. These relationships remained significant even with correction for multiple
comparisons by controlling for false discovery rate (both corrected P-values <0.04). After
correction for premorbid intelligence (WTAR), mean cortical OEF remained associated with
processing speed (p = 0.50, uncorrected P = 0.02).
86
A
B
45
Sensorimotor
40
0
Uw
0
30
20
20
Control
45
30
25
25
C
Parietal
40
35
U_
45
Patient
Control
Patient
D
Prefrontal
45
40
40
35
35I-I
Mean across cortex
0_ 30
0_30
25
25
20
20
Control
Control
Patient
Patient
Figure 5.17. Box-plot representation of OEF in 14 controls and 23 patients with MS in the (a)
sensorimotor cortex, (b) parietal cortex, (c) prefrontal cortex, and (d) averaged across the entire
cortex. The asterisks indicate significantly reduced OEF in all patients relative to healthy controls by
the Mann-Whitney test before correction for multiple comparisons.
A
B
2
2
C
U)
U)
(D
(0
0)
C
0
1
1
C
4
0
+
*
0
*
XI
0)
-1
-1
-2
-2
20
25
30
35
40
Mean OEF (%)
20
25
30
35
40
Mean OEF (%)
Figure 5.18. Scatter plot of the correlation between mean cortical OEF with (a) processing speed Zscore (p = 0.50, uncorrected P = 0.01), and with (b) executive function Z-score (p = 0.48,
uncorrected P = 0.03). These relationships remained significant even after correction for multiple
comparisons by controlling the false discovery rate (corrected P < 0.04).
87
5.5.
Discussion
5.5.1. OEF findings in multiple sclerosis and reproducibility
This study represents the first application of a susceptibility-based MRI method to quantify
OEF in the cortex of patients with MS. This approach exploited the paramagnetic effect of dHb
on high-resolution phase images at 7 T to estimate absolute cortical OEF in pial veins. We
observed a 3.4% absolute reduction of mean cortical OEF in a heterogeneous cohort including
patients with CIS, RRMS, and SPMS relative to healthy controls. Using T 2-based TRUST MRI
scans, Ge et al. measured a similar decrease of 5.7% global OEF in the sagittal sinus of
patients with RRMS compared to controls (Ge et al., 2012).
Ultra-high field MRI (7 T and above) allows for a two- to three-fold increase in SNR over 3 T
MRI (Vaughan et al., 2001), which enables improved spatial resolution of images. Through use
of 7 T phase images, we achieved voxel volume of 0.11 mm 3 in this study, compared to 0.50
mm 3 across the brain (Fan et al., 2012) and 0.48 mm 3 in the neck (Li et al., 2012) from previous
3 T investigations. As such, we were able to assess OEF in individual pial veins of smaller size,
and monitor regional oxygenation in patients from the sensorimotor, parietal, and prefrontal
areas. There was no difference in oxygenation across the regions in all subjects, which
suggests that OEF reduction presents diffusely across the cortex. This finding agrees with
literature PET studies that found no regional distribution to metabolic abnormalities in MS, and
posited that cerebral metabolic hypoactivity in the disease may reflect autoimmune changes in
the brain independent of local, visible lesions (Brooks et al., 1984; Sun et al., 1998). These
diffuse OEF changes may also explain the excellent intra-observer (COV = 2.1%), interobserver (COV = 5.2%), and scan-rescan (COV = 5.9%) agreement of mean cortical OEF
values in controls and patients, even though vessels were selected independently for all
reproducibility analyses.
Our observation of a negative relationship between OEF and EDSS is consistent with the
positive association between oxygen saturation and EDSS identified by Ge et al (Ge et al.,
2012). Both studies suggest that patients with reduced oxygen extraction tend to have higher
clinical disability. In contrast to Ge et al., our work did not find a relationship between
oxygenation and structural WM damage. This may be due to our small sample size, in which
half of the patients have earlier disease. Physical disability and accumulated macroscopic tissue
damage are less likely in early stages, such that a direct relationship between EDSS or
structural damage (LV, LC) and OEF changes may be difficult to detect. Furthermore, our LC
88
analyses were based only on focal cortical lesions. Pathological studies have demonstrated that
diffuse subpial demyelination tends to involve larger cortical surfaces than other types of CLs
(Peterson et al., 2001). Surface-based techniques for assessing diffuse cortical changes
(Cohen-Adad et al., 2011) may provide more sensitive measures to investigate the relationship
between cortical lesions and oxygen dysfunction in MS.
On the other hand, OEF measurements in this work were most strongly associated with
cognitive performance in patients, in particular processing speed. Cognitive changes may
appear early in the disease, even at the first demyelinating event (Amato et al., 2006), and our
MS population includes early patients with relatively few neuropsychological deficits. One
limitation of the study is our relatively small sample size, which precluded the use of multiple
regression models to assess the independent contributions of metabolic changes and structural
damage to EDSS and cognition. Future work will compare the relative contribution of OEF with
markers of structural damage that have been implicated in cognitive decline. These include both
subcortical pathology including macroscopic WM lesions (Filippi et al., 2010) and thalamic
volume (Houtchens et al., 2007); as well as cortical pathology including cortical lesions (Amato
et al., 2007; Calabrese et al., 2009; Nielsen et al., 2013; Rinaldi et al., 2010), in particular those
extending into the adjacent WM (Nelson et al., 2011), and cortical atrophy (Benedict et al.,
2002; Morgen et al., 2006).
5.5.2. Limitations of the work
Despite the gains in SNR at 7 T, ultra-high field imaging also poses unique challenges due
to more severe main field and excitation inhomogeneities, as well as decreased T 2* relaxation
rates (Peters et al., 2007). These phenomena lead to signal loss and difficult phase unwrapping
problems, especially in cortical areas near the air-tissue susceptibility interfaces. Such artifacts
cannot be easily addressed because the flow-compensation gradients, necessary for accurate
phase estimation in venous blood, constrain the minimum TE and TE spacing of the acquisition.
At these TE's, the signal loss prevented OEF measurements for veins located in the
orbitofrontal and temporal lobes in this study. Alternative acquisitions with z-shim compensation
can tailor excitation pulses to mitigate through-plane signal loss due to bulk susceptibility effects
and enable oxygenation assessment in such challenging regions (Deng et al., 2009; Du et al.,
2007).
89
Regional evaluation of OEF can also be facilitated by quantitative susceptibility mapping
(QSM) methods which reconstruct 3D susceptibility distributions from phase images (de
Rochefort et al., 2010a; Schweser et al., 2011b). These methods have successfully been
applied in patients with MS for monitoring of iron deposition in the basal ganglia (Langkammer
et al., 2013) and the combined effects of iron accumulation and demyelination within WM
lesions (Chen et al., 2013b). In future work, QSM techniques could be optimized to quantify
susceptibility in narrow structures such as vessels and enable robust mapping of OEF along
veins of arbitrary orientation and geometry in the brain vasculature (Fan et al., 2013). Improved
susceptibility quantification would also mitigate potential measurement error due to vessel tilt,
which can create up to -6% error for tilt angles less than 150 (Fan et al., 2012; Langham et al.,
2009a) in this study, thus reducing the variance on OEF values.
In this work, oxygenation measurements are interpreted to reflect cortical function. Although
the exact tissue origin of venous blood for selected vessels are unknown and may represent
both GM and WM contributions, we believe that OEF measurements in this study primarily
reflect cortical function. Physiologically, the flow in GM is nearly three times that in WM, as
measured by several modalities including 150 PET imaging (ratio = 2.9) (Huang et al., 1983) and
dynamic MRI with contrast agent (ratio = 2.7) (Ostergaard et al., 1996). This flow difference
suggests that OEF values in venous blood are weighted more heavily by GM function.
Furthermore, many of the vessels chosen for analysis in this work are located on the pial
surface of the brain, which drain superficially from cortical tissue (Duvernoy et al., 1981);
whereas the WM and deep GM structures drain medially into the internal cerebral veins and
straight sinus (Schlesinger, 1939).
5.5.3. Conclusions
The results of our study are meaningful in light of recent interest in energy metabolism
failure in the brain as a biomarker of MS disease progression and disability (Lazzarino et al.,
2010; Paling et al., 2011). Pathological examinations have revealed increased neuronal energy
demand in response to demyelination in MS (Blokhin et al., 2008; Mahad et al., 2009; Witte et
al., 2009). Concurrently, impaired mitochondrial function (Ciccarelli et al., 2010; Dutta et al.,
2006; Mahad et al., 2008) makes it difficult to meet this increased metabolic demand. This
pathophysiology, in conjunction with oxidative damage caused by inflammation (Fischer et al.,
2013), may contribute to energy failure and ultimately cell death in cortical lesions. A current
limitation to this study is the lack of blood flow measurements, which can be coupled with OEF
90
to directly quantify the cerebral metabolic rate of oxygen (CMRO 2). Nonetheless, previous
studies using phase-contrast and dynamic susceptibility contrast MRI have found either
unchanged or reduced brain perfusion in MS (Ge et al., 2012; Inglese et al., 2008), indicating
overall decrease in CMRO 2. Thus, OEF changes describe a key component of hypometabolism
in MS that is specific to neurons rather than glial cells (Kasischke et al., 2004), and offer a
potential independent biomarker to understand the metabolic underpinnings of cognitive
impairment in patients.
Although our findings need to be reproduced in a larger patient sample, this preliminary
study suggests that cortical OEF reflects MS disease and disability. Evaluation of metabolic and
structural damage at ultra-high resolution 7 T MRI can provide complementary information in
monitoring MS disease course, and potentially in assessing treatments.
91
92
6. Conclusions and Future Directions
This thesis contributes a new method to image brain oxygenation via susceptibility images
reconstructed from MRI phase. We evaluated the method at baseline and during hypercapnia
and found good consistency with literature PET and MRI values. We also demonstrated that
oxygenation imaging by susceptibility MRI has potential to detect subtle OEF alterations in a
disease state and relates to cognitive performance. However, several additional steps are
necessary to facilitate use of this method in the clinic. This chapter identifies extensions to
susceptibility-based oxygenation imaging that drive the tools toward efficient, clinical
application and to high-resolution implementation for basic neuroscience studies. The
aims proposed here result from collaborations with Professors Pablo lrarrdzaval and Cristian
Tejos at the Pontificia Universidad Cat6lica de Chile, Santiago, Chile.
6.1.
Modeling the vasculature for improved accuracy of OEF imaging
To estimate OEF across the brain vasculature, we implemented QSM to recover 3D
susceptibility distributions from single-orientation phase images. QSM enabled marked
improvement in OEF accuracy compared to MR susceptometry. However, due to the complex
and nonlinear relationship between X and MRI phase (4), residual OEF bias is evident for
vessels oriented at poorly conditioned angles both in numerical simulation and in vivo
(Chapter 3). Furthermore, partial volume effects may persist such that the susceptibility shift
between
narrow vessels and reference tissue is underestimated, and OEF is also
underestimated. To address these limitations, we propose use of vessel priors from vascular
models to refine oxygenation measurements along MR brain venograms.
6.1.1.
Impact
The phase image acquisition uses a gradient-echo sequence that forms the basis of
susceptibility-weighted imaging (SWI) routines that are increasingly popular in the clinic to
93
visualize small vessels (Idbaih et al., 2006; Sehgal et al., 2005a) and microbleeds
(Hammond et al., 2009b). Translation of the new QSM reconstructions to the clinic
would allow for quantitative evaluation of OEF from the same dataset acquired for
SWI contrast. This feature eliminates the need of a separate acquisition for oxygenation
imaging.
*
Previous studies have found that overall blood supply to the brain is an important indicator
of white-matter health with age, and that decline in CBF with age may exacerbate
deterioration of the connective structure of the brain (Chen et al., 2013a). Furthermore, it
has been suggested that reduced perfusion plays a role in cognitive decline in
neurodegenerative disorders such as Huntington's disease (Chen et al., 2012). The ability
to model small vessels may enable population-based mapping along vasculature in
the brain and detect subtle oxygenation changes associated with white-matter
disease. This could in turn improve our understanding of neurodegenerative pathologies,
progression, and evaluation of potential therapies.
6.1.2. Proposed approach
*
A vascular anatomical model can provide prior information to refine QSM values. The
current QSM regularization constrains the gradient of the underlying susceptibility map that
we wish to reconstruct to be sparse. However, this formulation does not contain information
about the structure of the vessels themselves. This vascular model could include information
about vessel diameter and circularity, and could be derived from phantom models (e.g.
currently in construction at the Pontificia Universidad Cat6lica de Chile); separate
venographic scans; or the gradient echo scan itself. For each vascular model, OEF could be
enforced to remain consistent along the length of each vessel segment, as expected for
physiology in vessels of this caliber.
*
One potential way to model the vascular anatomy is to perform vessel tracking on the
susceptibility map itself. This can be achieved with software already developed to
vectorize 3D vascular volumes, such as the Volumetric Image Data Analysis (VIDA)
software (Tsai et al., 2009). As additional information, vessel diameter is also automatically
estimated at each node (Fang et al., 2008).
94
6.1.3. Metrics of success
*
The accuracy of vascular models can be compared with standard venography MRI
methods (Kirchhof et al., 2002) such as contrast-enhanced imaging (Hu et al., 2007;
Tomasian et al., 2009), time-of-flight (Mattle et al., 1991), or phase-contrast scans (Vock et
al., 1991).
" OEF measurements should be robust to different vessel diameters and vessel tilt angles.
*
Oxygenation venograms should be self-consistent in physiology. For instance, as a sanity
check, oxygenation levels at branch points should be consistent for the vessel segments at
the junction. We will also compare with oxygenation values derived from other MRI physical
contrast mechanisms, including T2 relaxation. Recently, vessel-specific T2-relaxation under
spin tagging (TRUST) has observed increased resting SvO 2 in some regions of the brain,
such as Vein of Galen (Krishnamurthy et al., 2013a). This is inconsistent with our
observation of decreased resting SvO 2 in in the nearby straight sinus. With more
sophisticated modeling, we would like to compare regional OEF with TRUST in the same
scan for cross-calibration.
6.1.4. Potential pitfalls and alternative strategies
*
Existing vessel graphing methods were optimized for other modalities (e.g. optical imaging)
with higher signal-to-noise ratio (SNR). As such, they may not perform as well on relatively
noisy QSM images from MRI. This limitation creates the need for manual edits to adjust
node positions and vessel diameters on the vessel graph, which is manually tasking and not
amenable to clinical use. To minimize the need for these edits, we can preprocess QSM
maps (Sato et al., 1998) to improve SNR of MRI images before vessel tracking, or apply
more sophisticated segmentation routines that address disjoint interruptions in vascular
models (Forkert et al., 2012; Forkert et al., 2010).
*
For large vessels with fast blood flow, flow-induced phase distortions may arise in the
presence inhomogeneous susceptibility field gradients despite first-moment gradient
mulling. This artifact has led to underestimation by >20% absolute OEF from QSM
measurements (Xu et al., 2013) and may contribute to unwanted OEF variance in our
hypercapnia experiments (Chapter 4). Xu et al proposed to model and remove the flowinduced distortions through a quadratic fit of phase across echo times per voxel (Xu et al.,
95
2013). However, this approach suffers from low SNR because of the increased number of
unknowns in the quadratic fit relative to the standard linear fit. Instead, we could use
separate measurements of vessel flow from phase-contrast MRI to predict and mitigate
phase artifacts in large vessels.
6.2.
Fast, efficient acquisition of susceptibility to enable a clinical
oxygenation exam.
QSM reconstruction requires 3D volumetric coverage. Unfortunately, long echo times (TE)
for adequate image contrast and high isotropic resolution translates to lengthy acquisition times
for whole-brain imaging. For instance, Wu et al. estimated that 20 minutes are necessary to
acquire fully sampled images with 1-mm isotropic resolution and matrix size = 192x192x120
(Wu et al., 2012a). Even with use of parallel imaging to accelerate the acquisition (Griswold et
al., 2002), the oxygenation venograms in our preliminary study (at 0.6-mm isotropic resolution;
matrix size = 384x336x224) required 16 minutes of scan time. This long scan time renders
oxygenation imaging from susceptibility impractical for many clinical populations.
Although various under-sampling techniques such as parallel imaging and compressed sensing
(Lustig et al., 2007; Murphy et al., 2012) have been proposed to reduce scan times, there is little
understanding about potential artifacts and noise properties on phase images (Dietrich et al.,
2008), and ultimately on OEF estimates.
6.2.1. Impact
*
If we are successful in reducing the acquisition and reconstruction time of QSM, this may
enable oxygenation imaging in critical settings such as acute stroke and traumatic brain
injury. In acute stroke, imaging must be completed within a limited time window from onset
to decide whether to initiate endovascular therapy (Latchaw et al., 2009). Additionally, faster
QSM approaches would facilitate oxygenation studies in neonates and fetal imaging in
which movement artifacts are common. This speedup in acquisition will enable us to explore
the underlying mechanisms of complex metabolic changes in neonatal hypoxic ischemic
encephalopathy (De Vis et al., 2014; Dehaes et al., 2013; Liu et al., 2014).
96
Table 6.7. Accelerated acquisitions for QSM
Citation
Approach
Resolution
Acquisition time
Wu B et al; MRM 2012 (Wu
Compressed
1 x 1 x 1 mm 3
Not accelerated
Multi-shot spirals
1 x 1 x 1 mm
et al., 2012b)
Wu B et al; Neuroimage 2012
sensing
3
(Wu et al., 2012a)
Bilgic B et al.; submitted to
ISMRM
*
2.5 min (8x
speedup)
WAVE-CAIPI
1 x 1 x 2 mm 3
40 sec (3x3
speedup)
A desired outcome is to recommend optimal acquisition and reconstruction techniques
for QSM, for the purpose of absolute OEF oxygenation imaging.
6.2.2. Proposed approach
" We will evaluate OEF measurements from accelerated phase acquisitions, including parallel
imaging and partial Fourier approaches, in numerical simulation. Literature studies to
accelerate acquisitions for QSM are imaging are described in Table 6.7.
" For high resolution imaging with large matrix sizes, QSM reconstruction times can be on the
order of hours per dataset. Fast reconstruction at the MRI scanner is necessary for clinical
settings and can be accelerated with the use of graphical processing cards (Abuhashem et
al., 2012a). More recently, our group has proposed efficient -e2-regularized reconstruction of
QSM via variable splitting (Bilgic et al., 2013a), as well as an iterative approach to efficiently
solve -e- regularized QSM (Bilgic et al., 2013b). These methods achieve 5x and 15x speedup relative to the nonlinear conjugate gradient methods for 41- and -e2-regularized QSM,
respectively, and have been demonstrated on in vivo brain datasets from 3 T.
6.2.3. Metrics of success
"
In general, the SNR of images reconstructed by parallel imaging is decreased by the square
root of the acceleration factor as well as a coil-dependent geometry factor (G-factor) relative
to the original unaccelerated scan. In parallel imaging, a large G-factor results in noise
amplification that is spatially variable and depends on the (a) acceleration factor and (b) the
specific geometry of the radiofrequency coil array used (Breuer et al., 2009). A low G-factor
97
value (close to 1) indicates minimal loss in image quality, and supports use of highly
accelerated parallel imaging. An method to achieve high-quality images from parallel
imaging by 2D CAIPIRINHA-controlled aliasing has been shown (Setsompop et al., 2011). A
similar definition can be derived for phase G-factor to evaluate image quality from
accelerated QSM acquisitions.
6.3.
High-resolution estimation of the cerebral metabolic rate of
oxygen (CMRO 2) at 7 Tesla
The size of the smallest vessel along which we are able to reliably map susceptibility (and
thus, OEF) is intrinsically limited by the achievable spatial resolution of the phase image. These
measurements can take advantage of improved SNR to achieve high-resolution images at ultrahigh field (7 Tesla; 7 T and higher). Given this boost in resolution, we will be able to assess OEF
in smaller veins that are more indicative of local function (Vaughan et al., 2001). However,
robust implementation of the method at 7 T also brings unique technical challenges such as
more severe Bo and RF field inhomogeneities, and faster signal decay due to shorter T2 *
relaxation of tissue.
6.3.1. Impact
Given sufficient resolution, functional studies with our oxygenation method may be ultimately
limited by the hemodynamic response of the brain (Polimeni et al., 2010; Triantafyllou et al.,
2005). The variability of OEF measurements during activation tasks will then depend on the
local vascular distribution (Duvernoy et al., 1981) and spatial specificity of blood flow regulation
by vessels in response to neural function. This would avoid potential displacement of the BOLD
MRI signal relative to the activated brain region, and enable neuroscientists to characterize
oxygen metabolism in individual functional parcellations
6.3.2. Proposed approach
*
Translate the 3 T imaging protocol to 7 T, and increase the matrix size to achieve isotropic
0.4 - 0.5 mm isotropic spatial resolution (Deistung et al., 2013). These modifications
necessarily need to consider optimal T2* weighting at higher field strengths, noting that the
98
flow compensation gradients within the acquisition may restrict the minimum echo time and
minimum spacing between echoes.
*
New venous territory mapping MRI will also be implemented at 7 Tesla to determine the true
venous drainage territory of each identified vessel (Wong and Guo, 2013a). This knowledge
will improve the accuracy of CMRO 2 values when combining OEF in small vessels and
regional tissue CBF, especially on a refined spatial scale (Fan et al., 2012).
6.3.3. Metrics of success
*
To confirm the accuracy of 7 Tesla implementation of oxygenation imaging, we will compare
OEF in venograms collected at 3 Tesla and 7 Tesla in the same healthy volunteers, in scan
sequential scan sessions.
*
We will utilize and test the 7 Tesla implementation via a functional task, which requires
high spatial localization (Ances et al., 2008; Leontiev and Buxton, 2007). For this known
task, we will predict the expected oxygenation change, and thus the statistical power we
have to detect the desired OEF difference for a given SNR and resolution.
6.3.4. Potential pitfalls and alternative strategies
" Spatial phase unwrapping is more difficult at 7 Tesla (7 T) due to increased phase accrual
for the same echo times. To overcome this challenge, we will adopt more sophisticated
voxelwise phase unwrapping that uses information from multiple echoes, or pre-design
echoes which minimize the ambiguity of phase wraps (Dagher et al., 2014; Robinson et al.,
2013).
" Severe signal dropout is expected in the orbitofrontal areas of the brain due to dramatic
susceptibility differences between air and tissue at 7 Tesla. This signal is potentially
recovered by advanced acquisition methods including z-shim gradient pulses (Finsterbusch
et al., 2012) and simultaneous multi-slice scans (Anderson et al., 2013) designed for
functional BOLD scans.
*
In this work arterial spin labeling scans are adopted as standard MRI measurements for
flow. However, ASL scans may suffer from Bo and B1 field inhomogeneities at 7 Tesla and
reduced spin label efficiency if whole-body excitation is not available at ultra-high field
strengths (Ghariq et al., 2012). As a result, reliable ASL implementation at 7 Tesla may
99
require additional hardware such as dedicated neck coils for spin labeling (Shen and Duong,
2011).
*
Venous architecture may vary greatly between volunteers and is likely less stereotyped
relative to arterial territories. In addition, territories may overlap such that multiple vessels
may drain a single cortical region.
While considerable work is necessary to implement susceptibility imaging of oxygenation in the
brain in a clinical setting or at high-resolution, ongoing technical advancements will facilitate
emerging new applications of OEF imaging for physicians and scientists in the neurosciences.
100
7. References
Aanerud, J., Borghammer, P., Chakravarty, M.M., Vang, K., Rodell, A.B., Jonsdottir, K.Y.,
Moller, A., Ashkanian, M., Vafaee, M.S., Iversen, P., Johannsen, P., Gjedde, A., 2012. Brain
energy metabolism and blood flow differences in healthy aging. J Cereb Blood Flow Metab 32,
1177-1187.
Abuhashem, O.A., Bilgic, B., Adalsteinsson, E., 2012a. GPU-accelerated quantitative
susceptibility mapping., Proc Int Soc Magn Reson Med, Melbourne, Australia, p. 3442.
Abuhashem, O.A., Bilgic, B., Adalsteinsson, E., 2012b. GPU accelerated quantitative
susceptibility mapping., Proc Int Soc Magn Reson Med, Melbourne, Australia, p. 3442.
Adams, C.W., Poston, R.N., Buk, S.J., 1989. Pathology, histochemistry and
immunocytochemistry of lesions in acute multiple sclerosis. J Neurol Sci 92, 291-306.
Adeoye, 0., Hornung, R., Khatri, P., Kleindorfer, D., 2011. Recombinant tissue-type
plasminogen activator use for ischemic stroke in the United States: a doubling of treatment rates
over the course of 5 years. Stroke 42, 1952-1955.
Adriaanse, S.M., van Dijk, K.R., Ossenkoppele, R., Reuter, M., Tolboom, N., Zwan, M.D.,
Yaqub, M., Boellaard, R., Windhorst, A.D., van der Flier, W.M., Scheltens, P., Lammertsma,
A.A., Barkhof, F., van Berckel, B.N., 2014. The effect of amyloid pathology and glucose
metabolism on cortical volume loss over time in Alzheimer's disease. Eur J Nucl Med Mol
Imaging.
Albers, G.W., Thijs, V.N., Wechsler, L., Kemp, S., Schlaug, G., Skalabrin, E., Bammer, R.,
Kakuda, W., Lansberg, M.G., Shuaib, A., Coplin, W., Hamilton, S., Moseley, M., Marks, M.P.,
2006. Magnetic resonance imaging profiles predict clinical response to early reperfusion: the
diffusion and perfusion imaging evaluation for understanding stroke evolution (DEFUSE) study.
Ann Neurol 60, 508-517.
Alonzi, R., Padhani, A.R., Maxwell, R.J., Taylor, N.J., Stirling, J.J., Wilson, J.I., d'Arcy, J.A.,
Collins, D.J., Saunders, M.I., Hoskin, P.J., 2009. Carbogen breathing increases prostate cancer
oxygenation: a translational MRI study in murine xenografts and humans. Br J Cancer 100, 644648.
Alsop, D.C., Detre, J.A., Golay, X., Gunther, M., Hendrikse, J., Hernandez-Garcia, L., Lu, H.,
Macintosh, B.J., Parkes, L.M., Smits, M., van Osch, M.J., Wang, D.J., Wong, E.C., Zaharchuk,
G., 2014. Recommended implementation of arterial spin-labeled perfusion MRI for clinical
101
applications: A consensus of the ISMRM perfusion study group and the European consortium
for ASL in dementia. Magn Reson Med.
Amato, M.P., Portaccio, E., Goretti, B., Zipoli, V., Battaglini, M., Bartolozzi, M.L., Stromillo, M.L.,
Guidi, L., Siracusa, G., Sorbi, S., Federico, A., De Stefano, N., 2007. Association of neocortical
volume changes with cognitive deterioration in relapsing-remitting multiple sclerosis. Arch
Neurol 64, 1157-1161.
Amato, M.P., Zipoli, V., Portaccio, E., 2006. Multiple sclerosis-related cognitive changes: a
review of cross-sectional and longitudinal studies. J Neurol Sci 245, 41-46.
An, H., Ford, A.L., Chen, Y., Vo, K.D., Powers, W.J., Lee, J.M., Lin, W., 2013. MR
oxygenmetabolic index (omi) thresholds for delineating core, penumbra, and oligemia in acute
ischemic stroke patients., Int Stroke Conf, p. 181.
An, H., Lin, W., 2000. Quantitative measurements of cerebral blood oxygen saturation using
magnetic resonance imaging. J Cereb Blood Flow Metab 20, 1225-1236.
An, H., Liu, Q., Chen, Y., Lin, W., 2009. Evaluation of MR-derived cerebral oxygen metabolic
index in experimental hyperoxic hypercapnia, hypoxia, and ischemia. Stroke 40, 2165-2172.
An, H., Sen, S., Chen, Y., Powers, W.J., Lin, W., 2012. Noninvasive Measurements of Cerebral
Blood Flow, Oxygen Extraction Fraction, and Oxygen Metabolic Index in Human with Inhalation
of Air and Carbogen using Magnetic Resonance Imaging. Transl Stroke Res 3, 246-254.
Ances, B.M., Leontiev, 0., Perthen, J.E., Liang, C., Lansing, A.E., Buxton, R.B., 2008. Regional
differences in the coupling of cerebral blood flow and oxygen metabolism changes in response
to activation: implications for BOLD-fMRI. Neuroimage 39, 1510-1521.
Anderson, R.J., Poser, B.A., Stenger, V.A., 2013. Simultaneous multislice spectral-spatial
excitations for reduced signal loss susceptibility artifact in BOLD functional MRI. Magn Reson
Med.
Astrup, J., Siesjo, B.K., Symon, L., 1981. Thresholds in cerebral ischemia - the ischemic
penumbra. Stroke 12, 723-725.
Atalay, M.K., Reeder, S.B., Zerhouni, E.A., Forder, J.R., 1995. Blood oxygenation dependence
of T1 and T2 in the isolated, perfused rabbit heart at 4.7T. Magn Reson Med 34, 623-627.
Bandettini, P.A., Kwong, K.K., Davis, T.L., Tootell, R.B., Wong, E.C., Fox, P.T., Belliveau, J.W.,
Weisskoff, R.M., Rosen, B.R., 1997. Characterization of cerebral blood oxygenation and flow
changes during prolonged brain activation. Hum Brain Mapp 5, 93-109.
Bandettini, P.A., Wong, E.C., Jesmanowicz, A., Hinks, R.S., Hyde, J.S., 1994. Spin-echo and
gradient-echo EPI of human brain activation using BOLD contrast: a comparative study at 1.5 T.
NMR Biomed 7,12-20.
Banzett, R.B., Garcia, R.T., Moosavi, S.H., 2000. Simple contrivance "clamps" end-tidal PCO(2)
and PO(2) despite rapid changes in ventilation. J Appi Physiol (1985) 88, 1597-1600.
102
Baron, J.C., Bousser, M.G., Rey, A., Guillard, A., Comar, D., Castaigne, P., 1981. Reversal of
focal "misery-perfusion syndrome" by extra-intracranial arterial bypass in hemodynamic cerebral
ischemia. A case study with 150 positron emission tomography. Stroke 12, 454-459.
Baron, J.C., Frackowiak, R.S., Herholz, K., Jones, T., Lammertsma, A.A., Mazoyer, B.,
Wienhard, K., 1989. Use of PET methods for measurement of cerebral energy metabolism and
hemodynamics in cerebrovascular disease. J Cereb Blood Flow Metab 9, 723-742.
Baudelet, C., Gallez, B., 2002. How does blood oxygen level-dependent (BOLD) contrast
correlate with oxygen partial pressure (p02) inside tumors? Magn Reson Med 48, 980-986.
Beal, M.F., 2003. Mitochondria, oxidative damage, and inflammation in Parkinson's disease.
Ann N Y Acad Sci 991, 120-131.
Beaney, R.P., Brooks, D.J., Leenders, K.L., Thomas, D.G., Jones, T., Halnan, K.E., 1985. Blood
flow and oxygen utilisation in the contralateral cerebral cortex of patients with untreated
intracranial tumours as studied by positron emission tomography, with observations on the
effect of decompressive surgery. J Neurol Neurosurg Psychiatry 48, 310-319.
Benedict, R.H., Bakshi, R., Simon, J.H., Priore, R., Miller, C., Munschauer, F., 2002. Frontal
cortex atrophy predicts cognitive impairment in multiple sclerosis. J Neuropsychiatry Clin
Neurosci 14, 44-51.
Bilgic, B., Chatnuntawech, I., Fan, A.P., Setsompop, K., Cauley, S.F., Wald, L.L., Adalsteinsson,
E., 2013a. Fast Image Reconstruction with L2-Regularization. J Magn Reson Imaging.
Bilgic, B., Fan, A.P., Polimeni, J.R., Cauley, S.F., Bianciardi, M., Adalsteinsson, E., Wald, L.L.,
Setsompop, K., 2013b. Fast quantitative susceptibility mapping with L1-regularization and
automatic parameter selection. Magn Reson Med.
Bilgic, B., Pfefferbaum, A., Rohlfing, T., Sullivan, E.V., Adalsteinsson, E., 2012. MRI estimates
of brain iron concentration in normal aging using quantitative susceptibility mapping.
Neuroimage 59, 2625-2635.
Blockley, N.P., Griffeth, V.E., Buxton, R.B., 2012. A general analysis of calibrated BOLD
methodology for measuring CMRO2 responses: comparison of a new approach with existing
methods. Neuroimage 60, 279-289.
Blokhin, A., Vyshkina, T., Komoly, S., Kalman, B., 2008. Variations in mitochondrial DNA copy
numbers in MS brains. J Mol Neurosci 35, 283-287.
Bolar, D.S., Rosen, B.R., Evans, K.C., Sorensen, A.G., Adalsteinsson, E., 2010. Depression of
cortical gray matter CMRO2 in awake humans during hypercapnia. Proc Int Soc Magn Reson
Med, p. 516.
Bolar, D.S., Rosen, B.R., Sorensen, A.G., Adalsteinsson, E., 2011. QUantitative Imaging of
eXtraction of oxygen and TIssue consumption (QUIXOTIC) using venular-targeted velocityselective spin labeling. Magn Reson Med 66, 1550-1562.
103
Borghammer, P., Jonsdottir, K.Y., Cumming, P., Ostergaard, K., Vang, K., Ashkanian, M.,
Vafaee, M., Iversen, P., Gjedde, A., 2008. Normalization in PET group comparison studies--the
importance of a valid reference region. Neuroimage 40, 529-540.
Boxerman, J.L., Hamberg, L.M., Rosen, B.R., Weisskoff, R.M., 1995. MR contrast due to
intravascular magnetic susceptibility perturbations. Magn Reson Med 34, 555-566.
Braun, R.D., Lanzen, J.L., Snyder, S.A., Dewhirst, M.W., 2001. Comparison of tumor and
normal tissue oxygen tension measurements using OxyLite or microelectrodes in rodents. Am J
Physiol Heart Circ Physiol 280, H2533-2544.
Bremmer, J.P., van Berckel, B.N., Persoon, S., Kappelle, L.J., Lammertsma, A.A., Kloet, R.,
Luurtsema, G., Rijbroek, A., Klijn, C.J., Boellaard, R., 2011. Day-to-day test-retest variability of
CBF, CMRO2, and OEF measurements using dynamic 150 PET studies. Mol Imaging Biol 13,
759-768.
Breuer, F.A., Kannengiesser, S.A., Blaimer, M., Seiberlich, N., Jakob, P.M., Griswold, M.A.,
2009. General formulation for quantitative G-factor calculation in GRAPPA reconstructions.
Magn Reson Med 62, 739-746.
Bright, M.G., Donahue, M.J., Duyn, J.H., Jezzard, P., Bulte, D.P., 2011. The effect of basal
vasodilation on hypercapnic and hypocapnic reactivity measured using magnetic resonance
imaging. J Cereb Blood Flow Metab 31, 426-438.
Brizel, D.M., Dodge, R.K., Clough, R.W., Dewhirst, M.W., 1999. Oxygenation of head and neck
cancer: changes during radiotherapy and impact on treatment outcome. Radiother Oncol 53,
113-117.
Brooks, D.J., Leenders, K.L., Head, G., Marshall, J., Legg, N.J., Jones, T., 1984. Studies on
regional cerebral oxygen utilisation and cognitive function in multiple sclerosis. J Neurol
Neurosurg Psychiatry 47, 1182-1191.
Brown, J.M., Wilson, W.R., 2004. Exploiting tumour hypoxia in cancer treatment. Nat Rev
Cancer 4, 437-447.
Brun, A., Englund, E., 1986. A white matter disorder in dementia of the Alzheimer type: a
pathoanatomical study. Ann Neurol 19, 253-262.
Bulte, D.P., Drescher, K., Jezzard, P., 2009. Comparison of hypercapnia-based calibration
techniques for measurement of cerebral oxygen metabolism with MRI. Magn Reson Med 61,
391-398.
Burns, A., Tyrrell, P., 1992. Association of age with regional cerebral oxygen utilization: a
positron emission tomography study. Age Ageing 21, 316-320.
Burrell, J.S., Walker-Samuel, S., Baker, L.C., Boult, J.K., Jamin, Y., Halliday, J., Waterton, J.C.,
Robinson, S.P., 2013. Exploring DeltaR(2) * and DeltaR(1) as imaging biomarkers of tumor
oxygenation. J Magn Reson Imaging 38, 429-434.
104
Buxton, R.B., Frank, L.R., Wong, E.C., Siewert, B., Warach, S., Edelman, R.R., 1998. A general
kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med 40,
383-396.
Calabrese, M., Agosta, F., Rinaldi, F., Mattisi, I., Grossi, P., Favaretto, A., Atzori, M., Bernardi,
V., Barachino, L., Rinaldi, L., Perini, P., Gallo, P., Filippi, M., 2009. Cortical lesions and atrophy
associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol 66,
1144-1150.
Cepeda-Prado, E., Popp, S., Khan, U., Stefanov, D., Rodriguez, J., Menalled, L.B., DowEdwards, D., Small, S.A., Moreno, H., 2012. R6/2 Huntington's disease mice develop early and
progressive abnormal brain metabolism and seizures. J Neurosci 32, 6456-6467.
Chalela, J.A., Kang, D.W., Luby, M., Ezzeddine, M., Latour, L.L., Todd, J.W., Dunn, B., Warach,
S., 2004. Early magnetic resonance imaging findings in patients receiving tissue plasminogen
activator predict outcome: Insights into the pathophysiology of acute stroke in the thrombolysis
era. Ann Neurol 55, 105-112.
Chen, J.J., Pike, G.B., 2010. Global cerebral oxidative metabolism during hypercapnia and
hypocapnia in humans: implications for BOLD fMRI. J Cereb Blood Flow Metab 30, 1094-1099.
Chen, J.J., Rosas, H.D., Salat, D.H., 2013a. The relationship between cortical blood flow and
sub-cortical white-matter health across the adult age span. PLoS One 8, e56733.
Chen, J.J., Salat, D.H., Rosas, H.D., 2012. Complex relationships between cerebral blood flow
and brain atrophy in early Huntington's disease. Neuroimage 59, 1043-1051.
Chen, W., Gauthier, S.A., Gupta, A., Comunale, J., Liu, T., Wang, S., Pei, M., Pitt, D., Wang, Y.,
2013b. Quantitative Susceptibility Mapping of Multiple Sclerosis Lesions at Various Ages.
Radiology.
Chen, Y., Wang, D.J., Detre, J.A., 2011. Test-retest reliability of arterial spin labeling with
common labeling strategies. J Magn Reson Imaging 33, 940-949.
Chiarelli, P.A., Bulte, D.P., Gallichan, D., Piechnik, S.K., Wise, R., Jezzard, P., 2007a. Flowmetabolism coupling in human visual, motor, and supplementary motor areas assessed by
magnetic resonance imaging. Magn Reson Med 57, 538-547.
Chiarelli, P.A., Bulte, D.P., Piechnik, S., Jezzard, P., 2007b. Sources of systematic bias in
hypercapnia-calibrated functional MRI estimation of oxygen metabolism. Neuroimage 34, 35-43.
Chiarelli, P.A., Bulte, D.P., Wise, R., Gallichan, D., Jezzard, P., 2007c. A calibration method for
quantitative BOLD fMRI based on hyperoxia. Neuroimage 37, 808-820.
Cho, Z.H., Ro, Y.M., Lim, T.H., 1992. NMR venography using the susceptibility effect produced
by deoxyhemoglobin. Magn Reson Med 28, 25-38.
Chopra, S., Foltz, W.D., Milosevic, M.F., Toi, A., Bristow, R.G., Menard, C., Haider, M.A., 2009.
Comparing oxygen-sensitive MRI (BOLD R2*) with oxygen electrode measurements: a pilot
study in men with prostate cancer. Int J Radiat Biol 85, 805-813.
105
Christen, T., Bolar, D.S., Zaharchuk, G., 2012a. Imaging Brain Oxygenation with MRI Using
Blood Oxygenation Approaches: Methods, Validation, and Clinical Applications. AJNR Am J
Neuroradiol.
Christen, T., Lemasson, B., Pannetier, N., Farion, R., Remy, C., Zaharchuk, G., Barbier, E.L.,
2012b. Is T2* enough to assess oxygenation? Quantitative blood oxygen level-dependent
analysis in brain tumor. Radiology 262, 495-502.
Christen, T., Schmiedeskamp, H., Straka, M., Bammer, R., Zaharchuk, G., 2012c. Measuring
brain oxygenation in humans using a multiparametric quantitative blood oxygenation level
dependent MRI approach. Magn Reson Med 68, 905-911.
Christen, T., Zaharchuk, G., Pannetier, N., Serduc, R., Joudiou, N., Vial, J.C., Remy, C.,
Barbier, E.L., 2012d. Quantitative MR estimates of blood oxygenation based on T2*: a
numerical study of the impact of model assumptions. Magn Reson Med 67, 1458-1468.
Churchill-Davidson, I., Sanger, C., Thomlinson, R.H., 1957. Oxygenation in radiotherapy. 11.
Clinical application. Br J Radiol 30, 406-422.
Ciccarelli, 0., Toosy, A.T., De Stefano, N., Wheeler-Kingshott, C.A., Miller, D.H., Thompson,
A.J., 2010. Assessing neuronal metabolism in vivo by modeling imaging measures. J Neurosci
30, 15030-15033.
Ciris, P.A., Qiu, M., Constable, R.T., 2013. Noninvasive MRI measurement of the absolute
cerebral blood volume-cerebral blood flow relationship during visual stimulation in healthy
humans. Magn Reson Med.
Cohen-Adad, J., Benner, T., Greve, D., Kinkel, R.P., Radding, A., Fischl, B., Rosen, B.R.,
Mainero, C., 2011. In vivo evidence of disseminated subpial T2* signal changes in multiple
sclerosis at 7 T: a surface-based analysis. Neuroimage 57, 55-62.
Dagher, J., Du, Y.P., 2012. Efficient and robust estimation of blood oxygenation levels in single
cerebral veins. Med Biol Eng Comput 50, 473-482.
Dagher, J., Reese, T., Bilgin, A., 2014. High-resolution, large dynamic range field map
estimation. Magn Reson Med 71, 105-117.
Dai, W., Garcia, D., de Bazelaire, C., Alsop, D.C., 2008. Continuous flow-driven inversion for
arterial spin labeling using pulsed radio frequency and gradient fields. Magn Reson Med 60,
1488-1497.
Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. 1.Segmentation and
surface reconstruction. Neuroimage 9, 179-194.
Dani, K.A., Santosh, C., Brennan, D., McCabe, C., Holmes, W.M., Condon, B., Hadley, D.M.,
Macrae, I.M., Shaw, M., Muir, K.W., 2010. T2*-weighted magnetic resonance imaging with
hyperoxia in acute ischemic stroke. Ann Neurol 68, 37-47.
Dani, K.A., Thomas, R.G., Chappell, F.M., Shuler, K., MacLeod, M.J., Muir, K.W., Wardlaw,
J.M., 2011. Computed tomography and magnetic resonance perfusion imaging in ischemic
stroke: definitions and thresholds. Ann Neurol 70, 384-401.
106
Davalos, A., Blanco, M., Pedraza, S., Leira, R., Castellanos, M., Pumar, J.M., Silva, Y., Serena,
J., Castillo, J., 2004. The clinical-DWI mismatch: a new diagnostic approach to the brain tissue
at risk of infarction. Neurology 62, 2187-2192.
Davis, T.L., Kwong, K.K., Weisskoff, R.M., Rosen, B.R., 1998. Calibrated functional MRI:
mapping the dynamics of oxidative metabolism. Proc Natl Acad Sci U S A 95, 1834-1839.
de Boorder, M.J., Hendrikse, J., van der Grond, J., 2004. Phase-contrast magnetic resonance
imaging measurements of cerebral autoregulation with a breath-hold challenge: a feasibility
study. Stroke 35, 1350-1354.
De Crespigny, A.J., Wendland, M.F., Derugin, N., Kozniewska, E., Moseley, M.E., 1992. Realtime observation of transient focal ischemia and hyperemia in cat brain. Magn Reson Med 27,
391-397.
de Rochefort, L., Liu, T., Kressler, B., Liu, J., Spincemaille, P., Lebon, V., Wu, J., Wang, Y.,
2010a. Quantitative susceptibility map reconstruction from MR phase data using bayesian
regularization: validation and application to brain imaging. Magn Reson Med 63, 194-206.
de Rochefort, L., Liu, T., Kressler, B., Liu, J., Spincemaille, P., Lebon, V., Wu, J.L., Wang, Y.,
2010b. Quantitative Susceptibility Map Reconstruction from MR Phase Data Using Bayesian
Regularization: Validation and Application to Brain Imaging. Magnetic Resonance in Medicine
63, 194-206.
De Vis, J.B., Petersen, E.T., Alderliesten, T., Groenendaal, F., de Vries, L.S., van Bel, F.,
Benders, M.J., Hendrikse, J., 2014. Non-invasive MRI measurements of venous oxygenation,
oxygen extraction fraction and oxygen consumption in neonates. Neuroimage.
Dearling, J.L., Lewis, J.S., Mullen, G.E., Rae, M.T., Zweit, J., Blower, P.J., 1998. Design of
hypoxia-targeting radiopharmaceuticals: selective uptake of copper-64 complexes in hypoxic
cells in vitro. Eur J Nucl Med 25, 788-792.
Dehaes, M., Aggarwal, A., Lin, P.Y., Rosa Fortuno, C., Fenoglio, A., Roche-Labarbe, N., Soul,
J.S., Franceschini, M.A., Grant, P.E., 2013. Cerebral oxygen metabolism in neonatal hypoxic
ischemic encephalopathy during and after therapeutic hypothermia. J Cereb Blood Flow Metab.
Deistung, A., Dittrich, E., Sedlacik, J., Rauscher, A., Reichenbach, J.R., 2009a. ToF-SWI:
simultaneous time of flight and fully flow compensated susceptibility weighted imaging. J Magn
Reson Imaging 29, 1478-1484.
Deistung, A., Dittrich, E., Sedlacik, J., Rauscher, A., Reichenbach, J.R., 2009b. ToF-SWI:
simultaneous time of flight and fully flow compensated susceptibility weighted imaging. Journal
of Magnetic Resonance Imaging 29, 1478-1484.
Deistung, A., Rauscher, A., Sedlacik, J., Stadler, J., Witoszynskyj, S., Reichenbach, J.R., 2008.
Susceptibility weighted imaging at ultra high magnetic field strengths: theoretical considerations
and experimental results. Magn Reson Med 60, 1155-1168.
Deistung, A., Schafer, A., Schweser, F., Biedermann, U., Turner, R., Reichenbach, J.R., 2013.
Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with
107
magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength. Neuroimage 65, 299314.
Deng, W., Yang, C., Alagappan, V., Wald, L.L., Boada, F.E., Stenger, V.A., 2009. Simultaneous
z-shim method for reducing susceptibility artifacts with multiple transmitters. Magn Reson Med
61, 255-259.
Deschner, E.E., Gray, L.H., 1959. Influence of oxygen tension on x-ray-induced chromosomal
damage in Ehrlich ascites tumor cells irradiated in vitro and in vivo. Radiat Res 11, 115-146.
Desgranges, B., Baron, J.C., de la Sayette, V., Petit-Taboue, M.C., Benali, K., Landeau, B.,
Lechevalier, B., Eustache, F., 1998. The neural substrates of memory systems impairment in
Alzheimer's disease. A PET study of resting brain glucose utilization. Brain 121 ( Pt 4), 611-631.
Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L.,
Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, R.J., 2006. An automated
labeling system for subdividing the human cerebral cortex on MRI scans into gyral based
regions of interest. Neuroimage 31, 968-980.
Dietrich, 0., Raya, J.G., Reeder, S.B., Ingrisch, M., Reiser, M.F., Schoenberg, S.O., 2008.
Influence of multichannel combination, parallel imaging and other reconstruction techniques on
MRI noise characteristics. Magn Reson Imaging 26, 754-762.
Donswijk, M.L., Jones, P.S., Guadagno, J.V., Carpenter, T.A., Moustafa, R.R., Fryer, T.D.,
Aigbirhio, F.l., Warburton, E.A., Baron, J.C., 2009. T2*-weighted MRI versus oxygen extraction
fraction PET in acute stroke. Cerebrovasc Dis 28, 306-313.
Du, Y.P., Dalwani, M., Wylie, K., Claus, E., Tregellas, J.R., 2007. Reducing susceptibility
artifacts in fMRI using volume-selective z-shim compensation. Magn Reson Med 57, 396-404.
Dunst, J., Stadler, P., Becker, A., Lautenschlager, C., Pelz, T., Hansgen, G., Molls, M., Kuhnt,
T., 2003. Tumor volume and tumor hypoxia in head and neck cancers. The amount of the
hypoxic volume is important. Strahlenther Onkol 179, 521-526.
Dutta, R., McDonough, J., Yin, X., Peterson, J., Chang, A., Torres, T., Gudz, T., Macklin, W.B.,
Lewis, D.A., Fox, R.J., Rudick, R., Mimics, K., Trapp, B.D., 2006. Mitochondrial dysfunction as a
cause of axonal degeneration in multiple sclerosis patients. Ann Neurol 59, 478-489.
Duvernoy, H.M., Delon, S., Vannson, J.L., 1981. Cortical Blood Vessels of the Human Brain.
Brain Research Bulletin 7, 519-579.
Duyn, J.H., van Gelderen, P., Li, T.Q., de Zwart, J.A., Koretsky, A.P., Fukunaga, M., 2007.
High-field MRI of brain cortical substructure based on signal phase. Proc Natl Acad Sci U S A
104, 11796-11801.
Elas, M., Williams, B.B., Parasca, A., Mailer, C., Pelizzari, C.A., Lewis, M.A., River, J.N.,
Karczmar, G.S., Barth, E.D., Halpern, H.J., 2003. Quantitative tumor oxymetric images from 4D
electron paramagnetic resonance imaging (EPRI): methodology and comparison with blood
oxygen level-dependent (BOLD) MRI. Magn Reson Med 49, 682-691.
108
Eustache, F., Rioux, P., Desgranges, B., Marchal, G., Petit-Taboue, M.C., Dary, M.,
Lechevalier, B., Baron, J.C., 1995. Healthy aging, memory subsystems and regional cerebral
oxygen consumption. Neuropsychologia 33, 867-887.
Evans, S.M., Koch, C.J., 2003. Prognostic significance of tumor oxygenation in humans. Cancer
Lett 195, 1-16.
Fan, A.P., Benner, T., Bolar, D.S., Rosen, B.R., Adalsteinsson, E., 2012. Phase-based regional
oxygen metabolism (PROM) using MRI. Magn Reson Med 67, 669-678.
Fan, A.P., Bilgic, B., Gagnon, L., Witzel, T., Bhat, H., Rosen, B.R., Adalsteinsson, E., 2013.
Quantitative oxygenation venography from MRI phase. Magn Reson Med.
Fang, Q., Sakadzic, S., Ruvinskaya, L., Devor, A., Dale, A.M., Boas, D.A., 2008. Oxygen
advection and diffusion in a three- dimensional vascular anatomical network. Opt Express 16,
17530-17541.
Fang, Q.Q., Boas, D.A., 2009. Tetrahedral Mesh Generation from Volumetric Binary and GrayScale Images. 2009 leee International Symposium on Biomedical Imaging: From Nano to
Macro, Vols 1 and 2, 1142-1145.
Farkas, E., Luiten, P.G., 2001. Cerebral microvascular pathology in aging and Alzheimer's
disease. Prog Neurobiol 64, 575-611.
Fernandez-Seara, M.A., Techawiboonwong, A., Detre, J.A., Wehrli, F.W., 2006. MR
susceptometry for measuring global brain oxygen extraction. Magn Reson Med 55, 967-973.
Filippi, M., Rocca, M.A., Benedict, R.H., DeLuca, J., Geurts, J.J., Rombouts, S.A., Ron, M.,
Comi, G., 2010. The contribution of MRI in assessing cognitive impairment in multiple sclerosis.
Neurology 75, 2121-2128.
Finsterbusch, J., Eippert, F., Buchel, C., 2012. Single, slice-specific z-shim gradient pulses
improve T2*-weighted imaging of the spinal cord. Neuroimage 59, 2307-2315.
Fischer, M.T., Wimmer, I., Hoftberger, R., Gerlach, S., Haider, L., Zrzavy, T., Hametner, S.,
Mahad, D., Binder, C.J., Krumbholz, M., Bauer, J., Bradl, M., Lassmann, H., 2013. Diseasespecific molecular events in cortical multiple sclerosis lesions. Brain 136, 1799-1815.
Fischl, B., Sereno, M.I., Dale, A.M., 1999. Cortical surface-based analysis. II: Inflation,
flattening, and a surface-based coordinate system. Neuroimage 9, 195-207.
Fleisher, A.S., Podraza, K.M., Bangen, K.J., Taylor, C., Sherzai, A., Sidhar, K., Liu, T.T., Dale,
A.M., Buxton, R.B., 2009. Cerebral perfusion and oxygenation differences in Alzheimer's
disease risk. Neurobiol Aging 30, 1737-1748.
Fog, T., 1965. The topography of plaques in multiple sclerosis with special reference to cerebral
plaques. Acta Neurol Scand Suppl 15, 1-161.
Forkert, N.D., Fiehler, J., Illies, T., Moller, D.P., Handels, H., Saring, D., 2012. 4D blood flow
visualization fusing 3D and 4D MRA image sequences. J Magn Reson Imaging 36, 443-453.
109
Forkert, N.D., Schmidt-Richberg, A., Saring, D., Illies, T., Fiehler, J., Handels, H., 2010. Closing
of interrupted vascular segmentations: an automatic approach based on shortest paths and
level sets., Proc SPIE, San Diego, California.
Frackowiak, R.S., Lenzi, G.L., Jones, T., Heather, J.D., 1980. Quantitative measurement of
regional cerebral blood flow and oxygen metabolism in man using 150 and positron emission
tomography: theory, procedure, and normal values. J Comput Assist Tomogr 4, 727-736.
Friedland, R.P., Budinger, T.F., Koss, E., Ober, B.A., 1985. Alzheimer's disease: anteriorposterior and lateral hemispheric alterations in cortical glucose utilization. Neurosci Lett 53, 235240.
Friston, K.J., Ashburner, J., Kiebel, S.J., Nichols, T.E., Penny, W.D., 2007. Statistical parametric
mapping: The analysis of functional brain images., London.
Fujibayashi, Y., Taniuchi, H., Yonekura, Y., Ohtani, H., Konishi, J., Yokoyama, A., 1997.
Copper-62-ATSM: a new hypoxia imaging agent with high membrane permeability and low
redox potential. J Nucl Med 38, 1155-1160.
Furlan, A.J., Eyding, D., Albers, G.W., AI-Rawi, Y., Lees, K.R., Rowley, H.A., Sachara, C.,
Soehngen, M., Warach, S., Hacke, W., 2006. Dose Escalation of Desmoteplase for Acute
Ischemic Stroke (DEDAS): evidence of safety and efficacy 3 to 9 hours after stroke onset.
Stroke 37, 1227-1231.
Gagel, B., Piroth, M., Pinkawa, M., Reinartz, P., Zimny, M., Kaiser, H.J., Stanzel, S., Asadpour,
B., Demirel, C., Hamacher, K., Coenen, H.H., Scholbach, T., Maneschi, P., DiMartino, E., Eble,
M.J., 2007. pO polarography, contrast enhanced color duplex sonography (CDS), [18F]
fluoromisonidazole and [18F] fluorodeoxyglucose positron emission tomography: validated
methods for the evaluation of therapy-relevant tumor oxygenation or only bricks in the puzzle of
tumor hypoxia? BMC Cancer 7, 113.
Gallagher, D., Belmonte, D., Deurenberg, P., Wang, Z., Krasnow, N., Pi-Sunyer, F.X.,
Heymsfield, S.B., 1998. Organ-tissue mass measurement allows modeling of REE and
metabolically active tissue mass. Am J Physiol 275, E249-258.
Gallagher, H.L., MacManus, D.G., Webb, S.L., Miller, D.H., 1997. A reproducible repositioning
method for serial magnetic resonance imaging studies of the brain in treatment trials for multiple
sclerosis. J Magn Reson Imaging 7, 439-441.
Gauthier, C.J., Desjardins-Crepeau, L., Madjar, C., Bherer, L., Hoge, R.D., 2012. Absolute
quantification of resting oxygen metabolism and metabolic reactivity during functional activation
using QUO2 MRI. Neuroimage 63, 1353-1363.
Gauthier, C.J., Hoge, R.D., 2012. Magnetic resonance imaging of resting OEF and CMRO(2)
using a generalized calibration model for hypercapnia and hyperoxia. Neuroimage 60, 12121225.
Gauthier, C.J., Hoge, R.D., 2013. A generalized procedure for calibrated MRI incorporating
hyperoxia and hypercapnia. Hum Brain Mapp 34, 1053-1069.
110
Gauthier, C.J., Madjar, C., Tancredi, F.B., Stefanovic, B., Hoge, R.D., 2011. Elimination of
visually evoked BOLD responses during carbogen inhalation: implications for calibrated MRI.
Neuroimage 54, 1001-1011.
Ge, Y., Zhang, Z., Lu, H., Tang, L., Jaggi, H., Herbert, J., Babb, J.S., Rusinek, H., Grossman,
R.I., 2012. Characterizing brain oxygen metabolism in patients with multiple sclerosis with T2relaxation-under-spin-tagging MRI. J Cereb Blood Flow Metab 32, 403-412.
Ge, Y., Zohrabian, V.M., Osa, E.O., Xu, J., Jaggi, H., Herbert, J., Haacke, E.M., Grossman, R.I.,
2009. Diminished visibility of cerebral venous vasculature in multiple sclerosis by susceptibilityweighted imaging at 3.0 Tesla. J Magn Reson Imaging 29, 1190-1194.
Geisler, B.S., Brandhoff, F., Fiehler, J., Saager, C., Speck, 0., Rother, J., Zeumer, H., Kucinski,
T., 2006. Blood-oxygen-level-dependent MRI allows metabolic description of tissue at risk in
acute stroke patients. Stroke 37, 1778-1784.
Germuska, M., Bulte, D.P., 2014. MRI measurement of oxygen extraction fraction, mean vessel
size and cerebral blood volume using serial hyperoxia and hypercapnia. Neuroimage 92C, 132142.
Ghariq, E., Teeuwisse, W.M., Webb, A.G., van Osch, M.J., 2012. Feasibility of
pseudocontinuous arterial spin labeling at 7 T with whole-brain coverage. MAGMA 25, 83-93.
Gibbs, J.M., Leenders, K.L., Wise, R.J., Jones, T., 1984. Evaluation of cerebral perfusion
reserve in patients with carotid-artery occlusion. Lancet 1, 182-186.
Golay, X., Silvennoinen, M.J., Zhou, J., Clingman, C.S., Kauppinen, R.A., Pekar, J.J., van Zij,
P.C., 2001. Measurement of tissue oxygen extraction ratios from venous blood T(2): increased
precision and validation of principle. Magn Reson Med 46, 282-291.
Goodwin, J.A., Vidyasagar, R., Balanos, G.M., Bulte, D., Parkes, L.M., 2009. Quantitative fMRI
using hyperoxia calibration: reproducibility during a cognitive Stroop task. Neuroimage 47, 573580.
Gray, L.H., Conger, A.D., Ebert, M., Hornsey, S., Scott, O.C., 1953. The concentration of
oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol 26,
638-648.
Griffeth, V.E., Buxton, R.B., 2011. A theoretical framework for estimating cerebral oxygen
metabolism changes using the calibrated-BOLD method: modeling the effects of blood volume
distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD
signal. Neuroimage 58, 198-212.
Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B.,
Haase, A., 2002. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn
Reson Med 47, 1202-1210.
Grohn, O.H., Lukkarinen, J.A., Oja, J.M., van ZijI, P.C., Ulatowski, J.A., Traystman, R.J.,
Kauppinen, R.A., 1998. Noninvasive detection of cerebral hypoperfusion and reversible
ischemia from reductions in the magnetic resonance imaging relaxation time, T2. J Cereb Blood
Flow Metab 18, 911-920.
111
Guo, J., Meakin, J.A., Jezzard, P., Wong, E.C., 2014. An optimized design to reduce eddy
current sensitivity in velocity-selective arterial spin labeling using symmetric BIR-8 pulses. Magn
Reson Med.
Guo, J., Wong, E.C., 2012. Venous oxygenation mapping using velocity-selective excitation and
arterial nulling. Magn Reson Med 68, 1458-1471.
Guyton, A.C., Hall, J.E., 2000. Red blood cells, anemia, and polycythemia. Saunders,
Philadelphia.
Haacke, E.M., Lai, S., Reichenbach, J.R., Kuppusamy, K., Hoogenraad, F.G., Takeichi, H., Lin,
W., 1997a. In vivo measurement of blood oxygen saturation using magnetic resonance imaging:
a direct validation of the blood oxygen level-dependent concept in functional brain imaging. Hum
Brain Mapp 5, 341-346.
Haacke, E.M., Lai, S., Reichenbach, J.R., Kuppusamy, K., Hoogenraad, F.G.C., Takeichi, H.,
Lin, W.L., 1997b. In vivo measurement of blood oxygen saturation using magnetic resonance
imaging: A direct validation of the blood oxygen level-dependent concept in functional brain
imaging. Hum Brain Mapp 5, 341-346.
Haacke, E.M., Tang, J., Neelavalli, J., Cheng, Y.C., 2010. Susceptibility mapping as a means to
visualize veins and quantify oxygen saturation. J Magn Reson Imaging 32, 663-676.
Haacke, E.M., Xu, Y., Cheng, Y.C., Reichenbach, J.R., 2004. Susceptibility weighted imaging
(SWI). Magn Reson Med 52, 612-618.
Hacke, W., Albers, G., AI-Rawi, Y., Bogousslavsky, J., Davalos, A., Eliasziw, M., Fischer, M.,
Furlan, A., Kaste, M., Lees, K.R., Soehngen, M., Warach, S., 2005. The Desmoteplase in Acute
Ischemic Stroke Trial (DIAS): a phase I MRI-based 9-hour window acute stroke thrombolysis
trial with intravenous desmoteplase. Stroke 36, 66-73.
Hacke, W., Furlan, A.J., Al-Rawi, Y., Davalos, A., Fiebach, J.B., Gruber, F., Kaste, M., Lipka,
L.J., Pedraza, S., Ringleb, P.A., Rowley, H.A., Schneider, D., Schwamm, L.H., Leal, J.S.,
Sohngen, M., Teal, P.A., Wilhelm-Ogunbiyi, K., Wintermark, M., Warach, S., 2009. Intravenous
desmoteplase in patients with acute ischaemic stroke selected by MRI perfusion-diffusion
weighted imaging or perfusion CT (DIAS-2): a prospective, randomised, double-blind, placebocontrolled study. Lancet Neurol 8, 141-150.
Hacke, W., Kaste, M., Bluhmki, E., Brozman, M., Davalos, A., Guidetti, D., Larrue, V., Lees,
K.R., Medeghri, Z., Machnig, T., Schneider, D., von Kummer, R., Wahlgren, N., Toni, D., 2008.
Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke. N EngI J Med 359, 13171329.
Hakim, A.M., Pokrupa, R.P., Villanueva, J., Diksic, M., Evans, A.C., Thompson, C.J., Meyer, E.,
Yamamoto, Y.L., Feindel, W.H., 1987. The effect of spontaneous reperfusion on metabolic
function in early human cerebral infarcts. Ann Neurol 21, 279-289.
Hallac, R.R., Zhou, H., Pidikiti, R., Song, K., Stojadinovic, S., Zhao, D., Solberg, T., Peschke,
P., Mason, R.P., 2013. Correlations of noninvasive BOLD and TOLD MRI with pO and
relevance to tumor radiation response. Magn Reson Med.
112
Hammond, K.E., Lupo, J.M., Xu, D., Metcalf, M., Kelley, D.A., Pelletier, D., Chang, S.M.,
Mukherjee, P., Vigneron, D.B., Nelson, S.J., 2008. Development of a robust method for
generating 7.0 T multichannel phase images of the brain with application to normal volunteers
and patients with neurological diseases. Neuroimage 39, 1682-1692.
Hammond, K.E., Lupo, J.M., Xu, D., Veeraraghavan, S., Lee, H., Kincaid, A., Vigneron, D.B.,
Manley, G.T., Nelson, S.J., Mukherjee, P., 2009a. Microbleed Detection in Traumatic Brain
Injury at 3T and 7T: Comparing 2D and 3D Gradient-Recalled Echo (GRE) Imaging with
Susceptibility-Weighted Imaging (SWI). Proc Int Soc Magn Reson Med, p. 248.
Hammond, K.E., Lupo, J.M., Xu, D., Veeraraghavan, S., Lee, H., Kincaid, A., Vigneron, D.B.,
Manley, G.T., Nelson, S.J., Mukherjee, P., 2009b. Microbleed Detection in Traumatic Brain
Injury at 3T and 7T: Comparing 2D and 3D Gradient-Recalled Echo (GRE) Imaging with
Susceptibility-Weighted Imaging (SWI). Proc Int Soc Magn Reson Med, Honolulu, Hawaii., p.
248.
Hansen, P.C., 1998. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of
Linear Inversion. SIAM.
Hansen, P.C., 2000. The L-curve and its use in the numerical treatment of inverse problems.
Comput. inverse problems electrocardiol., 119-142.
Hattori, N., Bergsneider, M., Wu, H.M., Glenn, T.C., Vespa, P.M., Hovda, D.A., Phelps, M.E.,
Huang, S.C., 2004. Accuracy of a method using short inhalation of 0-15-0-2 for measuring
cerebral oxygen extraction fraction with PET in healthy humans. Journal of Nuclear Medicine
45, 765-770.
He, X., Yablonskiy, D.A., 2007. Quantitative BOLD: mapping of human cerebral deoxygenated
blood volume and oxygen extraction fraction: default state. Magn Reson Med 57, 115-126.
He, X., Zhu, M., Yablonskiy, D.A., 2008. Validation of oxygen extraction fraction measurement
by qBOLD technique. Magn Reson Med 60, 882-888.
Heijtel, D.F., Mutsaerts, H.J., Bakker, E., Schober, P., Stevens, M.F., Petersen, E.T., van
Berckel, B.N., Majoie, C.B., Booij, J., van Osch, M.J., Vanbavel, E., Boellaard, R., Lammertsma,
A.A., Nederveen, A.J., 2014. Accuracy and precision of pseudo-continuous arterial spin labeling
perfusion during baseline and hypercapnia: A head-to-head comparison with 0 HO positron
emission tomography. Neuroimage 92C, 182-192.
Heiss, W.D., Graf, R., 1994. The ischemic penumbra. Curr Opin Neurol 7, 11-19.
Heiss, W.D., Kracht, L., Grond, M., Rudolf, J., Bauer, B., Wienhard, K., Pawlik, G., 2000. Early
[(1 1)C]Flumazenil/H(2)O positron emission tomography predicts irreversible ischemic cortical
damage in stroke patients receiving acute thrombolytic therapy. Stroke 31, 366-369.
Heiss, W.D., Sobesky, J., 2008. Comparison of PET and DW/PW-MRI in acute ischemic stroke.
Keio J Med 57, 125-131.
Herscovitch, P., Mintun, M.A., Raichle, M.E., 1985. Brain oxygen utilization measured with
oxygen-1 5 radiotracers and positron emission tomography: generation of metabolic images. J
Nucl Med 26, 416-417.
113
Herscovitch, P., Raichle, M.E., 1985. What is the correct value for the brain--blood partition
coefficient for water? J Cereb Blood Flow Metab 5, 65-69.
Hicks, R.J., Rischin, D., Fisher, R., Binns, D., Scott, A.M., Peters, L.J., 2005. Utility of FMISO
PET in advanced head and neck cancer treated with chemoradiation incorporating a hypoxiatargeting chemotherapy agent. Eur J Nucl Med Mol Imaging 32, 1384-1391.
Hino-Shishikura, A., Tateishi, U., Shibata, H., Yoneyama, T., Nishii, T., Torii, I., Tateishi, K.,
Ohtake, M., Kawahara, N., Inoue, T., 2014. Tumor hypoxia and microscopic diffusion capacity in
brain tumors: A comparison of Cu-Diacetyl-Bis (N4-Methylthiosemicarbazone) PET/CT and
diffusion-weighted MR imaging. Eur J Nucl Med Mol Imaging.
Hirata, K., Terasaka, S., Shiga, T., Hattori, N., Magota, K., Kobayashi, H., Yamaguchi, S.,
Houkin, K., Tanaka, S., Kuge, Y., Tamaki, N., 2012. (1)(8)F-Fluoromisonidazole positron
emission tomography may differentiate glioblastoma multiforme from less malignant gliomas.
Eur J Nucl Med Mol Imaging 39, 760-770.
Hock, C., Villringer, K., Muller-Spahn, F., Wenzel, R., Heekeren, H., Schuh-Hofer, S., Hofmann,
M., Minoshima, S., Schwaiger, M., Dirnagl, U., Villringer, A., 1997. Decrease in parietal cerebral
hemoglobin oxygenation during performance of a verbal fluency task in patients with
Alzheimer's disease monitored by means of near-infrared spectroscopy (NIRS)--correlation with
simultaneous rCBF-PET measurements. Brain Res 755, 293-303.
Hoge, R.D., Atkinson, J., Gill, B., Crelier, G.R., Marrett, S., Pike, G.B., 1999. Investigation of
BOLD signal dependence on cerebral blood flow and oxygen consumption: the
deoxyhemoglobin dilution model. Magn Reson Med 42, 849-863.
Holst, B., Siemonsen, S., Finsterbusch, J., Bester, M., Schippling, S., Martin, R., Fiehler, J.,
2009. T2' imaging indicates decreased tissue metabolism in frontal white matter of MS patients.
Mult Scler 15, 701-707.
Horvath, I., Sandor, N.T., Ruttner, Z., McLaughlin, A.C., 1994. Role of nitric oxide in regulating
cerebrocortical oxygen consumption and blood flow during hypercapnia. J Cereb Blood Flow
Metab 14, 503-509.
Hoskin, P.J., Carnell, D.M., Taylor, N.J., Smith, R.E., Stirling, J.J., Daley, F.M., Saunders, M.I.,
Bentzen, S.M., Collins, D.J., d'Arcy, J.A., Padhani, A.P., 2007. Hypoxia in prostate cancer:
correlation of BOLD-MRI with pimonidazole immunohistochemistry-initial observations. Int J
Radiat Oncol Biol Phys 68, 1065-1071.
Hossmann, K.A., 1994. Viability thresholds and the penumbra of focal ischemia. Ann Neurol 36,
557-565.
Houtchens, M.K., Benedict, R.H., Killiany, R., Sharma, J., Jaisani, Z., Singh, B., WeinstockGuttman, B., Guttmann, C.R., Bakshi, R., 2007. Thalamic atrophy and cognition in multiple
sclerosis. Neurology 69, 1213-1223.
Hu, H.H., Campeau, N.G., Huston, J., 3rd, Kruger, D.G., Haider, C.R., Riederer, S.J., 2007.
High-spatial-resolution contrast-enhanced MR angiography of the intracranial venous system
with fourfold accelerated two-dimensional sensitivity encoding. Radiology 243, 853-861.
114
Huang, S.C., Carson, R.E., Hoffman, E.J., Carson, J., MacDonald, N., Barrio, J.R., Phelps,
M.E., 1983. Quantitative measurement of local cerebral blood flow in humans by positron
computed tomography and 150-water. J Cereb Blood Flow Metab 3, 141-153.
Ibaraki, M., Miura, S., Shimosegawa, E., Sugawara, S., Mizuta, T., Ishikawa, A., Amano, M.,
2008. Quantification of cerebral blood flow and oxygen metabolism with 3-dimensional PET and
150: validation by comparison with 2-dimensional PET. J Nucl Med 49, 50-59.
Ibaraki, M., Shimosegawa, E., Miura, S., Takahashi, K., Ito, H., Kanno, I., Hatazawa, J., 2004.
PET measurements of CBF, OEF, and CMRO2 without arterial sampling in hyperacute ischemic
stroke: method and error analysis. Ann Nucl Med 18, 35-44.
Ibaraki, M., Shinohara, Y., Nakamura, K., Miura, S., Kinoshita, F., Kinoshita, T., 2010.
Interindividual variations of cerebral blood flow, oxygen delivery, and metabolism in relation to
hemoglobin concentration measured by positron emission tomography in humans. J Cereb
Blood Flow Metab 30, 1296-1305.
Idbaih, A., Boukobza, M., Crassard, I., Porcher, R., Bousser, M.G., Chabriat, H., 2006. MRI of
clot in cerebral venous thrombosis: high diagnostic value of susceptibility-weighted images.
Stroke 37, 991-995.
Inglese, M., Adhya, S., Johnson, G., Babb, J.S., Miles, L., Jaggi, H., Herbert, J., Grossman, R.I.,
2008. Perfusion magnetic resonance imaging correlates of neuropsychological impairment in
multiple sclerosis. J Cereb Blood Flow Metab 28, 164-171.
Ishii, K., Sasaki, M., Kitagaki, H., Sakamoto, S., Yamaji, S., Maeda, K., 1996. Regional
difference in cerebral blood flow and oxidative metabolism in human cortex. J Nucl Med 37,
1086-1088.
Ito, H., Ibaraki, M., Kanno, I., Fukuda, H., Miura, S., 2005a. Changes in cerebral blood flow and
cerebral oxygen metabolism during neural activation measured by positron emission
tomography: comparison with blood oxygenation level-dependent contrast measured by
functional magnetic resonance imaging. J Cereb Blood Flow Metab 25, 371-377.
Ito, H., Kanno, I., Fukuda, H., 2005b. Human cerebral circulation: positron emission tomography
studies. Ann Nucl Med 19, 65-74.
Ito, H., Kanno, I., Ibaraki, M., Hatazawa, J., Miura, S., 2003. Changes in human cerebral blood
flow and cerebral blood volume during hypercapnia and hypocapnia measured by positron
emission tomography. J Cereb Blood Flow Metab 23, 665-670.
Ito, M., Lammertsma, A.A., Wise, R.J., Bernardi, S., Frackowiak, R.S., Heather, J.D., McKenzie,
C.G., Thomas, D.G., Jones, T., 1982. Measurement of regional cerebral blood flow and oxygen
utilisation in patients with cerebral tumours using 150 and positron emission tomography:
analytical techniques and preliminary results. Neuroradiology 23, 63-74.
Jain, V., Abdulmalik, 0., Propert, K.J., Wehrli, F.W., 2011a. Investigating the magnetic
susceptibility properties of fresh human blood for noninvasive oxygen saturation quantification.
Magn Reson Med.
115
Jain, V., Abdulmalik, 0., Propert, K.J., Wehrli, F.W., 2012a. Investigating the magnetic
susceptibility properties of fresh human blood for noninvasive oxygen saturation quantification.
Magnetic Resonance in Medicine 68, 863-867.
Jain, V., Abdulmalik, 0., Propert, K.J., Wehrli, F.W., 2012b. Investigating the magnetic
susceptibility properties of fresh human blood for noninvasive oxygen saturation quantification.
Magn Reson Med 68, 863-867.
Jain, V., Langham, M.C., Floyd, T.F., Jain, G., Magland, J.F., Wehrli, F.W., 2011b. Rapid
magnetic resonance measurement of global cerebral metabolic rate of oxygen consumption in
humans during rest and hypercapnia. J Cereb Blood Flow Metab 31, 1504-1512.
Jain, V., Langham, M.C., Wehrli, F.W., 2010. MRI estimation of global brain oxygen
consumption rate. J Cereb Blood Flow Metab 30, 1598-1607.
Jain, V., Magland, J., Langham, M., Wehrli, F.W., 2012c. High temporal resolution in vivo blood
oximetry via projection-based T(2) measurement. Magn Reson Med.
Jenkinson, M., 2003. Fast, automated, N-dimensional phase-unwrapping algorithm. Magn
Reson Med 49, 193-197.
Jerome, N.P., Hekmatyar, S.K., Kauppinen, R.A., 2014. Blood oxygenation level dependent,
blood volume, and blood flow responses to carbogen and hypoxic hypoxia in 9L rat gliomas as
measured by MRI. J Magn Reson Imaging 39, 110-119.
Jiang, L., Weatherall, P.T., McColl, R.W., Tripathy, D., Mason, R.P., 2013. Blood oxygenation
level-dependent (BOLD) contrast magnetic resonance imaging (MRI) for prediction of breast
cancer chemotherapy response: a pilot study. J Magn Reson Imaging 37, 1083-1092.
Jones, T., Chesler, D.A., Ter-Pogossian, M.M., 1976. The continuous inhalation of oxygen-15
for assessing regional oxygen extraction in the brain of man. Br J Radiol 49, 339-343.
Jordan, J.E., Pelc, N.J., Enzmann, D.R., 1994. Velocity and flow quantitation in the superior
sagittal sinus with ungated and cine (gated) phase-contrast MR imaging. J Magn Reson
Imaging 4, 25-28.
Kajimoto, K., Moriwaki, H., Yamada, N., Hayashida, K., Kobayashi, J., Miyashita, K., Naritomi,
H., 2003. Cerebral hemodynamic evaluation using perfusion-weighted magnetic resonance
imaging: comparison with positron emission tomography values in chronic occlusive carotid
disease. Stroke 34, 1662-1666.
Karimi, M., Golchin, N., Tabbal, S.D., Hershey, T., Videen, T.O., Wu, J., Usche, J.W., Revilla,
F.J., Hartlein, J.M., Wernle, A.R., Mink, J.W., Perlmutter, J.S., 2008. Subthalamic nucleus
stimulation-induced regional blood flow responses correlate with improvement of motor signs in
Parkinson disease. Brain 131, 2710-2719.
Kasischke, K.A., Vishwasrao, H.D., Fisher, P.J., Zipfel, W.R., Webb, W.W., 2004. Neural activity
triggers neuronal oxidative metabolism followed by astrocytic glycolysis. Science 305, 99-103.
Kastrup, A., Kruger, G., Glover, G.H., Moseley, M.E., 1999a. Assessment of cerebral oxidative
metabolism with breath holding and fMRI. Magn Reson Med 42, 608-611.
116
Kastrup, A., Kruger, G., Glover, G.H., Neumann-Haefelin, T., Moseley, M.E., 1999b. Regional
variability of cerebral blood oxygenation response to hypercapnia. Neuroimage 10, 675-681.
Kastrup, A., Kruger, G., Neumann-Haefelin, T., Moseley, M.E., 2001. Assessment of
cerebrovascular reactivity with functional magnetic resonance imaging: comparison of CO(2)
and breath holding. Magn Reson Imaging 19, 13-20.
Kety, S.S., 1956. Human cerebral blood flow and oxygen consumption as related to aging. J
Chronic Dis 3, 478-486.
Kety, S.S., Schmidt, C.F., 1948. The Effects of Altered Arterial Tensions of Carbon Dioxide and
Oxygen on Cerebral Blood Flow and Cerebral Oxygen Consumption of Normal Young Men. J
Clin Invest 27, 484-492.
Kidd, D., Barkhof, F., McConnell, R., Algra, P.R., Allen, I.V., Revesz, T., 1999. Cortical lesions
in multiple sclerosis. Brain 122 ( Pt 1), 17-26.
Kidwell, C.S., Alger, J.R., Saver, J.L., 2003. Beyond mismatch: evolving paradigms in imaging
the ischemic penumbra with multimodal magnetic resonance imaging. Stroke 34, 2729-2735.
Kidwell, C.S., Alger, J.R., Saver, J.L., 2004. Evolving paradigms in neuroimaging of the
ischemic penumbra. Stroke 35, 2662-2665.
Kidwell, C.S., Saver, J.L., Mattiello, J., Starkman, S., Vinuela, F., Duckwiler, G., Gobin, Y.P.,
Jahan, R., Vespa, P., Kalafut, M., Alger, J.R., 2000. Thrombolytic reversal of acute human
cerebral ischemic injury shown by diffusion/perfusion magnetic resonance imaging. Ann Neurol
47, 462-469.
Kim, H., Catana, C., Arabasz, G., Bolar, D.S., Gerstner, E.R., Batchelor, T.T., Jain, R.K., Rosen,
B.R., Sorensen, A.G., 2011 a. A Simultaneous Measurement of Relative CMRO2 with MRI and
FMISO Uptake with PET in Glioblastoma. Proc Int Soc Magn Reson Med, Montreal, Canada, p.
183.
Kim, H., Catana, C., Mouridsen, K., D.S., B., Gerstner, E.R., Batchelor, T.T., Jain, R.K., Rosen,
B.R., Sorensen, A.G., 2011b. Evaluation of relative CMRO2 from BOLD and CBF changes in
hyperoxia: Significant increase of oxygen consumption rate in glioblastoma. Montreal, Canada,
p. 186.
Kim, S.G., Rostrup, E., Larsson, H.B., Ogawa, S., Paulson, O.B., 1999. Determination of
relative CMRO2 from CBF and BOLD changes: significant increase of oxygen consumption rate
during visual stimulation. Magn Reson Med 41, 1152-1161.
Kirchhof, K., Welzel, T., Jansen, 0., Sartor, K., 2002. More reliable noninvasive visualization of
the cerebral veins and dural sinuses: comparison of three MR angiographic techniques.
Radiology 224, 804-810.
Koopmans, P.J., Manniesing, R., Niessen, W.J., Viergever, M.A., Barth, M., 2008. MR
venography of the human brain using susceptibility weighted imaging at very high field strength.
MAGMA 21, 149-158.
117
Kressler, B., de Rochefort, L., Liu, T., Spincemaille, P., Jiang, Q., Wang, Y., 2010. Nonlinear
regularization for per voxel estimation of magnetic susceptibility distributions from MRI field
maps. IEEE Trans Med Imaging 29, 273-281.
Krishnamurthy, L.C., Liu, P., Ge, Y., Lu, H., 2013a. Vessel-specific quantification of blood
oxygenation with T -relaxation-under-phase-contrast MRI. Magn Reson Med, 0.
Krishnamurthy, L.C., Liu, P., Xu, F., Uh, J., Dimitrov, I., Lu, H., 2013b. Dependence of blood T
on oxygenation at 7 T: In vitro calibration and in vivo application. Magn Reson Med.
Krohn, K.A., Link, J.M., Mason, R.P., 2008. Molecular imaging of hypoxia. J Nucl Med 49 Suppl
2, 129S-148S.
Kubota, K., Tada, M., Yamada, S., Hori, K., Saito, S., Iwata, R., Sato, K., Fukuda, H., Ido, T.,
1999. Comparison of the distribution of fluorine-18 fluoromisonidazole, deoxyglucose and
methionine in tumour tissue. Eur J Nucl Med 26, 750-757.
Kung, A.L., Wang, S., KIco, J.M., Kaelin, W.G., Livingston, D.M., 2000. Suppression of tumor
growth through disruption of hypoxia-inducible transcription. Nat Med 6, 1335-1340.
Kurtzke, J.F., 1983. Rating neurologic impairment in multiple sclerosis: an expanded disability
status scale (EDSS). Neurology 33, 1444-1452.
Kuwabara, Y., Ichiya, Y., Sasaki, M., Yoshida, T., Fukumura, T., Masuda, K., Fujii, K., Fukui, M.,
1998. PET evaluation of cerebral hemodynamics in occlusive cerebrovascular disease pre- and
postsurgery. J Nucl Med 39, 760-765.
Kwong, K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E., Weisskoff, R.M., Poncelet, B.P.,
Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., et al., 1992. Dynamic magnetic
resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad
Sci U S A 89, 5675-5679.
Lammertsma, A.A., Wise, R.J., Jones, T., 1983. In vivo measurements of regional cerebral
blood flow and blood volume in patients with brain tumours using positron emission tomography.
Acta Neurochir (Wien) 69, 5-13.
Langham, M.C., Magland, J.F., Epstein, C.L., Floyd, T.F., Wehrli, F.W., 2009a. Accuracy and
precision of MR blood oximetry based on the long paramagnetic cylinder approximation of large
vessels. Magn Reson Med 62, 333-340.
Langham, M.C., Magland, J.F., Floyd, T.F., Wehrli, F.W., 2009b. Retrospective correction for
induced magnetic field inhomogeneity in measurements of large-vessel hemoglobin oxygen
saturation by MR susceptometry. Magn Reson Med 61, 626-633.
Langkammer, C., Liu, T., Khalil, M., Enzinger, C., Jehna, M., Fuchs, S., Fazekas, F., Wang, Y.,
Ropele, S., 2013. Quantitative susceptibility mapping in multiple sclerosis. Radiology 267, 551559.
Latchaw, R.E., Alberts, M.J., Lev, M.H., Connors, J.J., Harbaugh, R.E., Higashida, R.T.,
Hobson, R., Kidwell, C.S., Koroshetz, W.J., Mathews, V., Villablanca, P., Warach, S., Walters,
118
B., 2009. Recommendations for imaging of acute ischemic stroke: a scientific statement from
the American Heart Association. Stroke 40, 3646-3678.
Lawrentschuk, N., Poon, A.M., Foo, S.S., Putra, L.G., Murone, C., Davis, I.D., Bolton, D.M.,
Scott, A.M., 2005. Assessing regional hypoxia in human renal tumours using 18Ffluoromisonidazole positron emission tomography. BJU Int 96, 540-546.
Lazzarino, G., Amorini, A.M., Eikelenboom, M.J., Killestein, J., Belli, A., Di Pietro, V., Tavazzi,
B., Barkhof, F., Polman, C.H., Uitdehaag, B.M., Petzold, A., 2010. Cerebrospinal fluid ATP
metabolites in multiple sclerosis. Mult Scler 16, 549-554.
Lee, J.M., Ford, A.L., An, H., Chen, Y., Vo, K.D., Powers, W.J., Lin, W., 2013. Mr-oxygen
metabolic index (OMI) is superior to diffusion-perfusion mismatch for predicting tissue fate in the
ischemic penumbra., Int Stroke Conf, p. 12.
Lee, J.M., Vo, K.D., An, H., Celik, A., Lee, Y., Hsu, C.Y., Lin, W., 2003. Magnetic resonance
cerebral metabolic rate of oxygen utilization in hyperacute stroke patients. Ann Neurol 53, 227232.
Lee, S.T., Scott, A.M., 2007. Hypoxia positron emission tomography imaging with 18ffluoromisonidazole. Semin Nucl Med 37, 451-461.
Leenders, K.L., 1994. PET: blood flow and oxygen consumption in brain tumors. J Neurooncol
22, 269-273.
Leenders, K.L., Frackowiak, R.S., Quinn, N., Marsden, C.D., 1986. Brain energy metabolism
and dopaminergic function in Huntington's disease measured in vivo using positron emission
tomography. Mov Disord 1, 69-77.
Leenders, K.L., Perani, D., Lammertsma, A.A., Heather, J.D., Buckingham, P., Healy, M.J.,
Gibbs, J.M., Wise, R.J., Hatazawa, J., Herold, S., et al., 1990. Cerebral blood flow, blood
volume and oxygen utilization. Normal values and effect of age. Brain 113 ( Pt 1), 27-47.
Lenzi, G.L., Frackowiak, R.S., Jones, T., Heather, J.D., Lammertsma, A.A., Rhodes, C.G.,
Pozzilli, C., 1981. CMRO2 and CBF by the oxygen-15 inhalation technique. Results in normal
volunteers and cerebrovascular patients. Eur Neurol 20, 285-290.
Leontiev, 0., Buxton, R.B., 2007. Reproducibility of BOLD, perfusion, and CMRO2
measurements with calibrated-BOLD fMRI. Neuroimage 35, 175-184.
Lewis, J.S., McCarthy, D.W., McCarthy, T.J., Fujibayashi, Y., Welch, M.J., 1999. Evaluation of
64Cu-ATSM in vitro and in vivo in a hypoxic tumor model. J Nucl Med 40, 177-183.
Li, C., Langham, M.C., Epstein, C.L., Magland, J.F., Wu, J., Gee, J., Wehrli, F.W., 2012.
Accuracy of the cylinder approximation for susceptometric measurement of intravascular
oxygen saturation. Magn Reson Med 67, 808-813.
Li, W., Wu, B., Liu, C., 2011. Quantitative susceptibility mapping of human brain reflects spatial
variation in tissue composition. Neuroimage 55, 1645-1656.
119
Lin, A.L., Fox, P.T., Yang, Y., Lu, H., Tan, L.H., Gao, J.H., 2008. Evaluation of MRI models in
the measurement of CMRO2 and its relationship with CBF. Magn Reson Med 60, 380-389.
Lin, W., An, H., Ford, A.L., Chen, Y., Vo, K.D., Powers, W.J., Lee, J.M., 2013. MR imaging of
oxygen extraction and neurovascular coupling. Stroke 44, S61-64.
Liu, C., Li, W., Wu, B., Jiang, Y., Johnson, G.A., 2012a. 3D fiber tractography with susceptibility
tensor imaging. Neuroimage 59, 1290-1298.
Liu, J., Liu, T., de Rochefort, L., Ledoux, J., Khalidov, I., Chen, W.W., Tsiouris, A.J., Wisnieff,
C., Spincemaille, P., Prince, M.R., Wang, Y., 2012b. Morphology enabled dipole inversion for
quantitative susceptibility mapping using structural consistency between the magnitude image
and the susceptibility map. Neuroimage 59, 2560-2568.
Liu, P., Huang, H., Rollins, N., Chalak, L.F., Jeon, T., Halovanic, C., Lu, H., 2014. Quantitative
assessment of global cerebral metabolic rate of oxygen (CMRO2) in neonates using MRI. NMR
Biomed 27, 332-340.
Liu, P., Xu, F., Lu, H., 2013. Test-retest reproducibility of a rapid method to measure brain
oxygen metabolism. Magn Reson Med 69, 675-681.
Liu, T., Khalidov, I., de Rochefort, L., Spincemaille, P., Liu, J., Tsiouris, A.J., Wang, Y., 2011 a. A
novel background field removal method for MRI using projection onto dipole fields (PDF). NMR
Biomed 24,1129-1136.
Liu, T., Liu, J., de Rochefort, L., Spincemaille, P., Khalidov, I., Ledoux, J.R., Wang, Y., 2011b.
Morphology Enabled Dipole Inversion (MEDI) from a Single-Angle Acquisition: Comparison with
COSMOS in Human Brain Imaging. Magnetic Resonance in Medicine 66, 777-783.
Liu, T., Spincemaille, P., de Rochefort, L., Kressler, B., Wang, Y., 2009. Calculation of
Susceptibility Through Multiple Orientation Sampling (COSMOS): A Method for Conditioning the
Inverse Problem From Measured Magnetic Field Map to Susceptibility Source Image in MRI.
Magnetic Resonance in Medicine 61, 196-204.
Liu, T., Surapaneni, K., Lou, M., Cheng, L., Spincemaille, P., Wang, Y., 2012c. Cerebral
microbleeds: burden assessment by using quantitative susceptibility mapping. Radiology 262,
269-278.
Lu, H., Clingman, C., Golay, X., van ZijI, P.C., 2004. Determining the longitudinal relaxation time
(T1) of blood at 3.0 Tesla. Magn Reson Med 52, 679-682.
Lu, H., Ge, Y., 2008. Quantitative evaluation of oxygenation in venous vessels using T2Relaxation-Under-Spin-Tagging MRI. Magn Reson Med 60, 357-363.
Lu, H., Xu, F., Grgac, K., Liu, P., Qin, Q., van Zijl, P., 2012. Calibration and validation of TRUST
MRI for the estimation of cerebral blood oxygenation. Magn Reson Med 67, 42-49.
Lu, H., Zhao, C., Ge, Y., Lewis-Amezcua, K., 2008. Baseline blood oxygenation modulates
response amplitude: Physiologic basis for intersubject variations in functional MRI signals. Magn
Reson Med 60, 364-372.
120
Lustig, M., Donoho, D., Pauly, J.M., 2007. Sparse MRI: The application of compressed sensing
for rapid MR imaging. Magn Reson Med 58, 1182-1195.
Macey, P.M., Alger, J.R., Kumar, R., Macey, K.E., Woo, M.A., Harper, R.M., 2003. Global BOLD
MRI changes to ventilatory challenges in congenital central hypoventilation syndrome. Respir
Physiol Neurobiol 139, 41-50.
Magistretti, P.J., Pellerin, L., 1996. Cellular mechanisms of brain energy metabolism. Relevance
to functional brain imaging and to neurodegenerative disorders. Ann N Y Acad Sci 777, 380387.
Magistretti, P.J., Pellerin, L., 1999. Cellular mechanisms of brain energy metabolism and their
relevance to functional brain imaging. Philos Trans R Soc Lond B Biol Sci 354, 1155-1163.
Mahad, D., Ziabreva, I., Lassmann, H., Turnbull, D., 2008. Mitochondrial defects in acute
multiple sclerosis lesions. Brain 131, 1722-1735.
Mahad, D.J., Ziabreva, I., Campbell, G., Lax, N., White, K., Hanson, P.S., Lassmann, H.,
Turnbull, D.M., 2009. Mitochondrial changes within axons in multiple sclerosis. Brain 132, 11611174.
Mainero, C., Benner, T., Radding, A., van der Kouwe, A., Jensen, R., Rosen, B.R., Kinkel, R.P.,
2009. In vivo imaging of cortical pathology in multiple sclerosis using ultra-high field MRI.
Neurology 73, 941-948.
Marchal, G., Rioux, P., Petit-Taboue, M.C., Sette, G., Travere, J.M., Le Poec, C., Courtheoux,
P., Derlon, J.M., Baron, J.C., 1992. Regional cerebral oxygen consumption, blood flow, and
blood volume in healthy human aging. Arch Neurol 49, 1013-1020.
Mark, C.I., Fisher, J.A., Pike, G.B., 2011. Improved fMRI calibration: precisely controlled
hyperoxic versus hypercapnic stimuli. Neuroimage 54, 1102-1111.
Mark, C.I., Pike, G.B., 2012. Indication of BOLD-specific venous flow-volume changes from
precisely controlled hyperoxic vs. hypercapnic calibration. J Cereb Blood Flow Metab 32, 709719.
Marques, J.P., Bowtell, R., 2005. Application of a fourier-based method for rapid calculation of
field inhomogeneity due to spatial variation of magnetic susceptibility. Concepts in Magnetic
Resonance Part B-Magnetic Resonance Engineering 25B, 65-78.
Martin, C., Jones, M., Martindale, J., Mayhew, J., 2006. Haemodynamic and neural responses
to hypercapnia in the awake rat. Eur J Neurosci 24, 2601-2610.
Mattle, H.P., Wentz, K.U., Edelman, R.R., Wallner, B., Finn, J.P., Barnes, P., Atkinson, D.J.,
Kleefield, J., Hoogewoud, H.M., 1991. Cerebral venography with MR. Radiology 178, 453-458.
McKay, L.C., Evans, K.C., Frackowiak, R.S., Corfield, D.R., 2003. Neural correlates of voluntary
breathing in humans. J Appl Physiol (1985) 95, 1170-1178.
121
McPhail, L.D., Robinson, S.P., 2010. Intrinsic susceptibility MR imaging of chemically induced
rat mammary tumors: relationship to histologic assessment of hypoxia and fibrosis. Radiology
254, 110-118.
Mees, G., Dierckx, R., Vangestel, C., Van de Wiele, C., 2009. Molecular imaging of hypoxia with
radiolabelled agents. Eur J Nucl Med Mol Imaging 36, 1674-1686.
Meltzer, C.C., Cantwell, M.N., Greer, P.J., Ben-Eliezer, D., Smith, G., Frank, G., Kaye, W.H.,
Houck, P.R., Price, J.C., 2000. Does cerebral blood flow decline in healthy aging? A PET study
with partial-volume correction. J Nucl Med 41, 1842-1848.
Menon, C., Fraker, D.L., 2005. Tumor oxygenation status as a prognostic marker. Cancer Lett
221, 225-235.
Mintun, M.A., Raichle, M.E., Martin, W.R., Herscovitch, P., 1984. Brain oxygen utilization
measured with 0-15 radiotracers and positron emission tomography. J Nucl Med 25, 177-187.
Morgen, K., Sammer, G., Courtney, S.M., Wolters, T., Melchior, H., Blecker, C.R., Oschmann,
P., Kaps, M., Vaitl, D., 2006. Evidence for a direct association between cortical atrophy and
cognitive impairment in relapsing-remitting MS. Neuroimage 30, 891-898.
Morita, N., Harada, M., Uno, M., Matsubara, S., Matsuda, T., Nagahiro, S., Nishitani, H., 2008.
Ischemic findings of T2*-weighted 3-tesla MRI in acute stroke patients. Cerebrovasc Dis 26,
367-375.
Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., Jack, C.R., Jagust, W., Trojanowski,
J.Q., Toga, A.W., Beckett, L., 2005. Ways toward an early diagnosis in Alzheimer's disease: the
Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement 1, 55-66.
Murphy, M., Alley, M., Demmel, J., Keutzer, K., Vasanawala, S., Lustig, M., 2012. Fast 1(1)SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and
clinically feasible runtime. IEEE Trans Med Imaging 31, 1250-1262.
Neelavalli, J., Jella, P.K., Krishnamurthy, U., Buch, S., Haacke, E.M., Yeo, L., Mody, S., Katkuri,
Y., Bahado-Singh, R., Hassan, S.S., Romero, R., Thomason, M.E., 2014. Measuring Venous
Blood Oxygenation in Fetal Brain Using Susceptibility-Weighted Imaging. J Magn Reson
Imaging 39, 998-1006.
Nehmeh, S.A., Lee, N.Y., Schroder, H., Squire, 0., Zanzonico, P.B., Erdi, Y.E., Greco, C.,
Mageras, G., Pham, H.S., Larson, S.M., Ling, C.C., Humm, J.L., 2008. Reproducibility of
intratumor distribution of (18)F-fluoromisonidazole in head and neck cancer. Int J Radiat Oncol
Biol Phys 70, 235-242.
Nelson, F., Datta, S., Garcia, N., Rozario, N.L., Perez, F., Cutter, G., Narayana, P.A., Wolinsky,
J.S., 2011. Intracortical lesions by 3T magnetic resonance imaging and correlation with
cognitive impairment in multiple sclerosis. Mult Scler 17, 1122-1129.
Neumann-Haefelin, T., Wittsack, H.J., Wenserski, F., Siebler, M., Seitz, R.J., Modder, U.,
Freund, H.J., 1999. Diffusion- and perfusion-weighted MRI. The DWI/PWI mismatch region in
acute stroke. Stroke 30, 1591-1597.
122
Nielsen, A.S., Kinkel, R.P., Madigan, N., Tinelli, E., Benner, T., Mainero, C., 2013. Contribution
of cortical lesion subtypes at 7T MRI to physical and cognitive performance in MS. Neurology
81, 641-649.
Nordsmark, M., Bentzen, S.M., Rudat, V., Brizel, D., Lartigau, E., Stadler, P., Becker, A., Adam,
M., Molls, M., Dunst, J., Terris, D.J., Overgaard, J., 2005. Prognostic value of tumor
oxygenation in 397 head and neck tumors after primary radiation therapy. An international multicenter study. Radiother Oncol 77, 18-24.
Nordsmark, M., Overgaard, J., 2004. Tumor hypoxia is independent of hemoglobin and
prognostic for loco-regional tumor control after primary radiotherapy in advanced head and neck
cancer. Acta Oncol 43, 396-403.
O'Connor, J.P., Naish, J.H., Jackson, A., Waterton, J.C., Watson, Y., Cheung, S., Buckley, D.L.,
McGrath, D.M., Buonaccorsi, G.A., Mills, S.J., Roberts, C., Jayson, G.C., Parker, G.J., 2009a.
Comparison of normal tissue R1 and R*2 modulation by oxygen and carbogen. Magn Reson
Med 61, 75-83.
O'Connor, J.P., Naish, J.H., Parker, G.J., Waterton, J.C., Watson, Y., Jayson, G.C.,
Buonaccorsi, G.A., Cheung, S., Buckley, D.L., McGrath, D.M., West, C.M., Davidson, S.E.,
Roberts, C., Mills, S.J., Mitchell, C.L., Hope, L., Ton, N.C., Jackson, A., 2009b. Preliminary
study of oxygen-enhanced longitudinal relaxation in MRI: a potential novel biomarker of
oxygenation changes in solid tumors. Int J Radiat Oncol Biol Phys 75, 1209-1215.
Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W., 1990. Brain magnetic resonance imaging with
contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 87, 9868-9872.
Oja, J.M., Gillen, J.S., Kauppinen, R.A., Kraut, M., van Zijl, P.C., 1999. Determination of oxygen
extraction ratios by magnetic resonance imaging. J Cereb Blood Flow Metab 19, 1289-1295.
Ostergaard, L., Sorensen, A.G., Kwong, K.K., Weisskoff, R.M., Gyldensted, C., Rosen, B.R.,
1996. High resolution measurement of cerebral blood flow using intravascular tracer bolus
passages. Part II: Experimental comparison and preliminary results. Magn Reson Med 36, 726736.
Paling, D., Golay, X., Wheeler-Kingshott, C., Kapoor, R., Miller, D., 2011. Energy failure in
multiple sclerosis and its investigation using MR techniques. J Neurol.
Pannetier, N.A., Sohlin, M., Christen, T., Schad, L., Schuff, N., 2013. Numerical modeling of
susceptibility-related MR signal dephasing with vessel size measurement: Phantom validation at
3T. Magn Reson Med.
Pantano, P., Baron, J.C., Lebrun-Grandie, P., Duquesnoy, N., Bousser, M.G., Comar, D., 1984.
Regional cerebral blood flow and oxygen consumption in human aging. Stroke 15, 635-641.
Parsons, M.W., Barber, P.A., Chalk, J., Darby, D.G., Rose, S., Desmond, P.M., Gerraty, R.P.,
Tress, B.M., Wright, P.M., Donnan, G.A., Davis, S.M., 2002. Diffusion- and perfusion-weighted
MRI response to thrombolysis in stroke. Ann Neurol 51, 28-37.
Parsons, M.W., Yang, Q., Barber, P.A., Darby, D.G., Desmond, P.M., Gerraty, R.P., Tress,
B.M., Davis, S.M., 2001. Perfusion magnetic resonance imaging maps in hyperacute stroke:
123
relative cerebral blood flow most accurately identifies tissue destined to infarct. Stroke 32, 15811587.
Pauling, L., Coryell, C.D., 1936. The Magnetic Properties and Structure of Hemoglobin,
Oxyhemoglobin and Carbonmonoxyhemoglobin. Proc Natl Acad Sci U S A 22, 210-216.
Peters, A.M., Brookes, M.J., Hoogenraad, F.G., Gowland, P.A., Francis, S.T., Morris, P.G.,
Bowtell, R., 2007. T2* measurements in human brain at 1.5, 3 and 7 T. Magn Reson Imaging
25, 748-753.
Peterson, J.W., Bo, L., Mork, S., Chang, A., Trapp, B.D., 2001. Transected neurites, apoptotic
neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann Neurol 50, 389400.
Plyavin, Y.A., Blum, E.Y., 1983. Magnetic parameters of blood cells and high-gradient
paramagnetic and diamagnetic phoresis. Magnetohydrodynamics 19, 349-359.
Polimeni, J.R., Fischl, B., Greve, D.N., Wald, L.L., 2010. Laminar analysis of 7T BOLD using an
imposed spatial activation pattern in human V1. Neuroimage 52, 1334-1346.
Polman, C.H., Reingold, S.C., Banwell, B., Clanet, M., Cohen, J.A., Filippi, M., Fujihara, K.,
Havrdova, E., Hutchinson, M., Kappos, L., Lublin, F.D., Montalban, X., O'Connor, P., SandbergWollheim, M., Thompson, A.J., Waubant, E., Weinshenker, B., Wolinsky, J.S., 2011. Diagnostic
criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69, 292-302.
Punwani, S., Ordidge, R.J., Cooper, C.E., Amess, P., Clemence, M., 1998. MRI measurements
of cerebral deoxyhaemoglobin concentration [dHb]--correlation with near infrared spectroscopy
(NIRS). NMR Biomed 11, 281-289.
Qin, Q., Grgac, K., van Zijl, P.C., 2011. Determination of whole-brain oxygen extraction fractions
by fast measurement of blood T(2) in the jugular vein. Magn Reson Med 65, 471-479.
Rajendran, J.G., Schwartz, D.L., O'Sullivan, J., Peterson, L.M., Ng, P., Scharnhorst, J.,
Grierson, J.R., Krohn, K.A., 2006. Tumor hypoxia imaging with [F-18] fluoromisonidazole
positron emission tomography in head and neck cancer. Clin Cancer Res 12, 5435-5441.
Rasey, J.S., Casciari, J.J., Hofstrand, P.D., Muzi, M., Graham, M.M., Chin, L.K., 2000.
Determining hypoxic fraction in a rat glioma by uptake of radiolabeled fluoromisonidazole.
Radiat Res 153, 84-92.
Rauscher, A., Barth, M., Herrmann, K.H., Witoszynskyj, S., Deistung, A., Reichenbach, J.R.,
2008. Improved elimination of phase effects from background field inhomogeneities for
susceptibility weighted imaging at high magnetic field strengths. Magn Reson Imaging 26, 11451151.
Rauscher, A., Sedlacik, J., Barth, M., Haacke, E.M., Reichenbach, J.R., 2005a. Nonnvasive
assessment of vascular architecture and function during modulated blood oxygenation using
susceptibility weighted magnetic resonance imaging. Magn Reson Med 54, 87-95.
124
Rauscher, A., Sedlacik, J., Barth, M., Mentzel, H.J., Reichenbach, J.R., 2005b. Magnetic
susceptibility-weighted MR phase imaging of the human brain. AJNR Am J Neuroradiol 26, 736742.
Reichenbach, J.R., Haacke, E.M., 2001. High-resolution BOLD venographic imaging: a window
into brain function. NMR Biomed 14, 453-467.
Remmele, S., Sprinkart, A.M., Muller, A., Traber, F., von Lehe, M., Gieseke, J., Flacke, S.,
Willinek, W.A., Schild, H.H., Senegas, J., Keupp, J., Murtz, P., 2013. Dynamic and simultaneous
MR measurement of R1 and R2* changes during respiratory challenges for the assessment of
blood and tissue oxygenation. Magn Reson Med 70, 136-146.
Rhodes, C.G., Wise, R.J., Gibbs, J.M., Frackowiak, R.S., Hatazawa, J., Palmer, A.J., Thomas,
D.G., Jones, T., 1983. In vivo disturbance of the oxidative metabolism of glucose in human
cerebral gliomas. Ann Neurol 14, 614-626.
Rijpkema, M., Kaanders, J.H., Joosten, F.B., van der Kogel, A.J., Heerschap, A., 2002. Effects
of breathing a hyperoxic hypercapnic gas mixture on blood oxygenation and vascularity of headand-neck tumors as measured by magnetic resonance imaging. Int J Radiat Oncol Biol Phys 53,
1185-1191.
Rinaldi, F., Calabrese, M., Grossi, P., Puthenparampil, M., Perini, P., Gallo, P., 2010. Cortical
lesions and cognitive impairment in multiple sclerosis. Neurol Sci 31, S235-237.
Rischin, D., Hicks, R.J., Fisher, R., Binns, D., Corry, J., Porceddu, S., Peters, L.J., 2006.
Prognostic significance of [18F]-misonidazole positron emission tomography-detected tumor
hypoxia in patients with advanced head and neck cancer randomly assigned to chemoradiation
with or without tirapazamine: a substudy of Trans-Tasman Radiation Oncology Group Study
98.02. J Clin Oncol 24, 2098-2104.
Rischin, D., Peters, L.J., O'Sullivan, B., Giralt, J., Fisher, R., Yuen, K., Trotti, A., Bernier, J.,
Bourhis, J., Ringash, J., Henke, M., Kenny, L., 2010. Tirapazamine, cisplatin, and radiation
versus cisplatin and radiation for advanced squamous cell carcinoma of the head and neck
(TROG 02.02, HeadSTART): a phase Ill trial of the Trans-Tasman Radiation Oncology Group. J
Clin Oncol 28, 2989-2995.
Robinson, S., Grabner, G., Witoszynskyj, S., Trattnig, S., 2011. Combining phase images from
multi-channel RF coils using 3D phase offset maps derived from a dual-echo scan. Magn Reson
Med 65, 1638-1648.
Robinson, S., Schodl, H., Trattnig, S., 2013. A method for unwrapping highly wrapped multiecho phase images at very high field: UMPIRE. Magn Reson Med.
Rockwell, S., Dobrucki, l.T., Kim, E.Y., Marrison, S.T., Vu, V.T., 2009. Hypoxia and radiation
therapy: past history, ongoing research, and future promise. Curr Mol Med 9, 442-458.
Rodgers, Z.B., Jain, V., Englund, E.K., Langham, M.C., Wehrli, F.W., 2013. High temporal
resolution MRI quantification of global cerebral metabolic rate of oxygen consumption in
response to apneic challenge. J Cereb Blood Flow Metab 33, 1514-1522.
125
Rodrigues, L.M., Howe, F.A., Griffiths, J.R., Robinson, S.P., 2004. Tumor R2* is a prognostic
indicator of acute radiotherapeutic response in rodent tumors. J Magn Reson Imaging 19, 482488.
Rohifing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A., 2010. The SR124 multichannel atlas of
normal adult human brain structure. Hum Brain Mapp 31, 798-819.
Rostrup, E., Knudsen, G.M., Law, I., Holm, S., Larsson, H.B., Paulson, O.B., 2005. The
relationship between cerebral blood flow and volume in humans. Neuroimage 24, 1-11.
Rostrup, E., Law, I., Blinkenberg, M., Larsson, H.B., Born, A.P., Holm, S., Paulson, O.B., 2000.
Regional differences in the CBF and BOLD responses to hypercapnia: a combined PET and
fMRI study. Neuroimage 11, 87-97.
Rother, J., Schellinger, P.D., Gass, A., Siebler, M., Villringer, A., Fiebach, J.B., Fiehler, J.,
Jansen, 0., Kucinski, T., Schoder, V., Szabo, K., Junge-Hulsing, G.J., Hennerici, M., Zeumer,
H., Sartor, K., Weiller, C., Hacke, W., 2002. Effect of intravenous thrombolysis on MRI
parameters and functional outcome in acute stroke <6 hours. Stroke 33, 2438-2445.
Roussel, S.A., van Bruggen, N., King, M.D., Gadian, D.G., 1995. Identification of collaterally
perfused areas following focal cerebral ischemia in the rat by comparison of gradient echo and
diffusion-weighted MRI. J Cereb Blood Flow Metab 15, 578-586.
Rudat, V., Stadler, P., Becker, A., Vanselow, B., Dietz, A., Wannenmacher, M., Molls, M.,
Dunst, J., Feldmann, H.J., 2001. Predictive value of the tumor oxygenation by means of p02
histography in patients with advanced head and neck cancer. Strahlenther Onkol 177, 462-468.
Salomir, R., De Senneville, B.D., Moonen, C.T.W., 2003. A fast calculation method for magnetic
field inhomogeneity due to an arbitrary distribution of bulk susceptibility. Concepts in Magnetic
Resonance Part B-Magnetic Resonance Engineering 19B, 26-34.
Sanganahalli, B.G., Herman, P., Behar, K.L., Blumenfeld, H., Rothman, D.L., Hyder, F., 2013.
Functional MRI and neural responses in a rat model of Alzheimer's disease. Neuroimage 79,
404-411.
Santosh, C., Brennan, D., McCabe, C., Macrae, I.M., Holmes, W.M., Graham, D.I., Gallagher,
L., Condon, B., Hadley, D.M., Muir, K.W., Gsell, W., 2008. Potential use of oxygen as a
metabolic biosensor in combination with T2*-weighted MRI to define the ischemic penumbra. J
Cereb Blood Flow Metab 28, 1742-1753.
Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.,
1998. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear
structures in medical images. Med Image Anal 2, 143-168.
Schellinger, P.D., Fiebach, J.B., Hacke, W., 2003. Imaging-based decision making in
thrombolytic therapy for ischemic stroke: present status. Stroke 34, 575-583.
Schlesinger, B., 1939. The venous drainage of the brain, with special reference to the Galenic
system. Brain 62, 274-291.
126
Schweser, F., Atterbury, M., Deistung, A., Lehr, B.W., Sommer, K., Reichenbach, J.R., 2011 a.
Harmonic phase subtraction methods are prone to B1 background components., Proc Int Soc
Magn Reson Med, Montreal, Quebec, Canada, p. 2657.
Schweser, F., Deistung, A., Lehr, B.W., Reichenbach, J.R., 2010. Differentiation between
diamagnetic and paramagnetic cerebral lesions based on magnetic susceptibility mapping. Med
Phys 37, 5165-5178.
Schweser, F., Deistung, A., Lehr, B.W., Reichenbach, J.R., 2011b. Quantitative imaging of
intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron
metabolism? Neuroimage 54, 2789-2807.
Schweser, F., Sommer, K., Deistung, A., Reichenbach, J.R., 2012. Quantitative susceptibility
mapping for investigating subtle susceptibility variations in the human brain. Neuroimage 62,
2083-2100.
Sedlacik, J., Kutschbach, C., Rauscher, A., Deistung, A., Reichenbach, J.R., 2008. Investigation
of the influence of carbon dioxide concentrations on cerebral physiology by susceptibilityweighted magnetic resonance imaging (SWI). Neuroimage 43, 36-43.
Sedlacik, J., Rauscher, A., Reichenbach, J.R., 2007. Obtaining blood oxygenation levels from
MR signal behavior in the presence of single venous vessels. Magn Reson Med 58, 1035-1044.
Sedlacik, J., Rauscher, A., Reichenbach, J.R., 2009. Quantification of modulated blood
oxygenation levels in single cerebral veins by investigating their MR signal decay. Z Med Phys
19, 48-57.
Sehgal, V., Delproposto, Z., Haacke, E.M., Tong, K.A., Wycliffe, N., Kido, D.K., Xu, Y.,
Neelavalli, J., Haddar, D., Reichenbach, J.R., 2005a. Clinical applications of neuroimaging with
susceptibility-weighted imaging. J Magn Reson Imaging 22, 439-450.
Sehgal, V., Delproposto, Z., Haacke, E.M., Tong, K.A., Wycliffe, N., Kido, D.K., Xu, Y.,
Neelavalli, J., Haddar, D., Reichenbach, J.R., 2005b. Clinical applications of neuroimaging with
susceptibility-weighted imaging. Journal of Magnetic Resonance Imaging 22, 439-450.
Setsompop, K., Gagoski, B.A., Polimeni, J.R., Wald, L.L., 2011. Wave-CAIPIRHINA: A method
for reducing g-factors in highly accelerated 3D acquisitions. Proc Int Soc Magn Reson Med,
Montreal, Canada, p. 478.
Severinghaus, J.W., 1989. Water vapor calibration errors in some capnometers: respiratory
conventions misunderstood by manufacturers? Anesthesiology 70, 996-998.
Shen, Q., Duong, T.Q., 2011. Background suppression in arterial spin labeling MRI with a
separate neck labeling coil. NMR Biomed 24, 1111-1118.
Shi, K., Maftei, C., Bayer, C., Astner, S., Gaertner, F., Vaupel, P., Schwaiger, M., Huang, S.C.,
Ziegler, S., 2012. Quantitative analysis of [18F] FMISO PET for tumor hypoxia: Correlation with
results using immunohistochemistry., Society of Nuclear Medicine Annual Meeting, p. 216.
127
Shmueli, K., de Zwart, J.A., van Gelderen, P., Li, T.Q., Dodd, S.J., Duyn, J.H., 2009a. Magnetic
Susceptibility Mapping of Brain Tissue In Vivo Using MRI Phase Data. Magnetic Resonance in
Medicine 62, 1510-1522.
Shmueli, K., de Zwart, J.A., van Gelderen, P., Li, T.Q., Dodd, S.J., Duyn, J.H., 2009b. Magnetic
susceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med 62, 15101522.
Sicard, K., Shen, Q., Brevard, M.E., Sullivan, R., Ferris, C.F., King, J.A., Duong, T.Q., 2003.
Regional cerebral blood flow and BOLD responses in conscious and anesthetized rats under
basal and hypercapnic conditions: implications for functional MRI studies. J Cereb Blood Flow
Metab 23, 472-481.
Sicard, K.M., Duong, T.Q., 2005. Effects of hypoxia, hyperoxia, and hypercapnia on baseline
and stimulus-evoked BOLD, CBF, and CMRO2 in spontaneously breathing animals.
Neuroimage 25, 850-858.
Siemonsen, S., Fitting, T., Thomalla, G., Horn, P., Finsterbusch, J., Summers, P., Saager, C.,
Kucinski, T., Fiehler, J., 2008. T2' imaging predicts infarct growth beyond the acute diffusionweighted imaging lesion in acute stroke. Radiology 248, 979-986.
Silvennoinen, M.J., Clingman, C.S., Golay, X., Kauppinen, R.A., van ZijI, P.C., 2003.
Comparison of the dependence of blood R2 and R2* on oxygen saturation at 1.5 and 4.7 Tesla.
Magn Reson Med 49, 47-60.
Smith, S.M., 2002a. Fast robust automated brain extraction. Hum Brain Mapp 17, 143-155.
Smith, S.M., 2002b. Fast robust automated brain extraction. Hum Brain Mapp 17, 143-155.
Sobesky, J., Zaro Weber, 0., Lehnhardt, F.G., Hesselmann, V., Neveling, M., Jacobs, A., Heiss,
W.D., 2005. Does the mismatch match the penumbra? Magnetic resonance imaging and
positron emission tomography in early ischemic stroke. Stroke 36, 980-985.
Sohlin, M.C., Schad, L.R., 2011. Susceptibility-related MR signal dephasing under nonstatic
conditions: experimental verification and consequences for qBOLD measurements. J Magn
Reson Imaging 33, 417-425.
Spees, W.M., Yablonskiy, D.A., Oswood, M.C., Ackerman, J.J.H., 2001. Water proton MR
properties of human blood at 1.5 Tesla: Magnetic susceptibility, T-1, T-2, T-2* and nonLorentzian signal behavior. Magnetic Resonance in Medicine 45, 533-542.
Stadelmann, C., Albert, M., Wegner, C., Bruck, W., 2008. Cortical pathology in multiple
sclerosis. Curr Opin Neurol 21, 229-234.
Stefanovic, B., Warnking, J.M., Pike, G.B., 2004. Hemodynamic and metabolic responses to
neuronal inhibition. Neuroimage 22, 771-778.
Sun, X., Tanaka, M., Kondo, S., Okamoto, K., Hirai, S., 1998. Clinical significance of reduced
cerebral metabolism in multiple sclerosis: a combined PET and MRI study. Ann Nucl Med 12,
89-94.
128
Takada, H., Nagata, K., Hirata, Y., Satoh, Y., Watahiki, Y., Sugawara, J., Yokoyama, E.,
Kondoh, Y., Shishido, F., Inugami, A., et al., 1992. Age-related decline of cerebral oxygen
metabolism in normal population detected with positron emission tomography. Neurol Res 14,
128-131.
Takasawa, M., Jones, P.S., Guadagno, J.V., Christensen, S., Fryer, T.D., Harding, S., Gillard,
J.H., Williams, G.B., Aigbirhio, F.I., Warburton, E.A., Ostergaard, L., Baron, J.C., 2008. How
reliable is perfusion MR in acute stroke? Validation and determination of the penumbra
threshold against quantitative PET. Stroke 39, 870-877.
Tallantyre, E.C., Brookes, M.J., Dixon, J.E., Morgan, P.S., Evangelou, N., Morris, P.G., 2008.
Demonstrating the perivascular distribution of MS lesions in vivo with 7-Tesla MRI. Neurology
70, 2076-2078.
Tamura, H., Hatazawa, J., Toyoshima, H., Shimosegawa, E., Okudera, T., 2002. Detection of
deoxygenation-related signal change in acute ischemic stroke patients by T2*-weighted
magnetic resonance imaging. Stroke 33, 967-971.
Tan, I.L., van Schijndel, R.A., Pouwels, P.J., van Walderveen, M.A., Reichenbach, J.R.,
Manoliu, R.A., Barkhof, F., 2000. MR venography of multiple sclerosis. AJNR Am J Neuroradiol
21, 1039-1042.
Tateishi, K., Tateishi, U., Sato, M., Yamanaka, S., Kanno, H., Murata, H., Inoue, T., Kawahara,
N., 2013. Application of 62Cu-diacetyl-bis (N4-methylthiosemicarbazone) PET imaging to
predict highly malignant tumor grades and hypoxia-inducible factor-1 alpha expression in
patients with glioma. AJNR Am J Neuroradiol 34, 92-99.
Tatum, J.L., Kelloff, G.J., Gillies, R.J., Arbeit, J.M., Brown, J.M., Chao, K.S., Chapman, J.D.,
Eckelman, W.C., Fyles, A.W., Giaccia, A.J., Hill, R.P., Koch, C.J., Krishna, M.C., Krohn, K.A.,
Lewis, J.S., Mason, R.P., Melillo, G., Padhani, A.R., Powis, G., Rajendran, J.G., Reba, R.,
Robinson, S.P., Semenza, G.L., Swartz, H.M., Vaupel, P., Yang, D., Croft, B., Hoffman, J., Liu,
G., Stone, H., Sullivan, D., 2006. Hypoxia: importance in tumor biology, noninvasive
measurement by imaging, and value of its measurement in the management of cancer therapy.
Int J Radiat Biol 82, 699-757.
Taylor, N.J., Baddeley, H., Goodchild, K.A., Powell, M.E., Thoumine, M., Culver, L.A., Stirling,
J.J., Saunders, M.I., Hoskin, P.J., Phillips, H., Padhani, A.R., Griffiths, J.R., 2001. BOLD MRI of
human tumor oxygenation during carbogen breathing. J Magn Reson Imaging 14, 156-163.
Teicher, B.A., 1994. Hypoxia and drug resistance. Cancer Metastasis Rev 13, 139-168.
Teicher, B.A., Holden, S.A., al-Achi, A., Herman, T.S., 1990. Classification of antineoplastic
treatments by their differential toxicity toward putative oxygenated and hypoxic tumor
subpopulations in vivo in the FSalIC murine fibrosarcoma. Cancer Res 50, 3339-3344.
Thomason, M.E., Foland, L.C., Glover, G.H., 2007. Calibration of BOLD fMRI using breath
holding reduces group variance during a cognitive task. Hum Brain Mapp 28, 59-68.
Thorwarth, D., Eschmann, S.M., Holzner, F., Paulsen, F., Alber, M., 2006. Combined uptake of
[1 8F]FDG and [1 8F]FMISO correlates with radiation therapy outcome in head-and-neck cancer
patients. Radiother Oncol 80, 151-156.
129
Thulborn, K.R., Waterton, J.C., Matthews, P.M., Radda, G.K., 1982. Oxygenation dependence
of the transverse relaxation time of water protons in whole blood at high field. Biochim Biophys
Acta 714, 265-270.
Tohgi, H., Yonezawa, H., Takahashi, S., Sato, N., Kato, E., Kudo, M., Hatano, K., Sasaki, T.,
1998. Cerebral blood flow and oxygen metabolism in senile dementia of Alzheimer's type and
vascular dementia with deep white matter changes. Neuroradiology 40, 131-137.
Tomasian, A., Salamon, N., Krishnam, M.S., Finn, J.P., Villablanca, J.P., 2009. 3D high-spatialresolution cerebral MR venography at 3T: a contrast-dose-reduction study. AJNR Am J
Neuroradiol 30, 349-355.
Triantafyllou, C., Hoge, R.D., Krueger, G., Wiggins, C.J., Potthast, A., Wiggins, G.C., Wald, L.L.,
2005. Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI
acquisition parameters. Neuroimage 26, 243-250.
Tsai, P.S., Kaufhold, J.P., Blinder, P., Friedman, B., Drew, P.J., Karten, H.J., Lyden, P.D.,
Kleinfeld, D., 2009. Correlations of Neuronal and Microvascular Densities in Murine Cortex
Revealed by Direct Counting and Colocalization of Nuclei and Vessels. Journal of Neuroscience
29, 14553-14570.
van Beek, A.H., Lagro, J., Olde-Rikkert, M.G., Zhang, R., Claassen, J.A., 2012. Oscillations in
cerebral blood flow and cortical oxygenation in Alzheimer's disease. Neurobiol Aging 33, 428
e421-431.
van der Kouwe, A.J., Benner, T., Salat, D.H., Fischl, B., 2008. Brain morphometry with
multiecho MPRAGE. Neuroimage 40, 559-569.
van Zijl, P.C., Eleff, S.M., Ulatowski, J.A., Oja, J.M., Ulug, A.M., Traystman, R.J., Kauppinen,
R.A., 1998. Quantitative assessment of blood flow, blood volume and blood oxygenation effects
in functional magnetic resonance imaging. Nat Med 4, 159-167.
Vaughan, J.T., Garwood, M., Collins, C.M., Liu, W., DelaBarre, L., Adriany, G., Andersen, P.,
Merkle, H., Goebel, R., Smith, M.B., Ugurbil, K., 2001. 7T vs. 4T: RF power, homogeneity, and
signal-to-noise comparison in head images. Magn Reson Med 46, 24-30.
Vaupel, P., Hockel, M., Mayer, A., 2007. Detection and characterization of tumor hypoxia using
p02 histography. Antioxid Redox Signal 9, 1221-1235.
Vaupel, P., Mayer, A., 2007. Hypoxia in cancer: significance and impact on clinical outcome.
Cancer Metastasis Rev 26, 225-239.
Vesely, A., Sasano, H., Volgyesi, G., Somogyi, R., Tesler, J., Fedorko, L., Grynspan, J.,
Crawley, A., Fisher, J.A., Mikulis, D., 2001. MRI mapping of cerebrovascular reactivity using
square wave changes in end-tidal PCO2. Magn Reson Med 45, 1011-1013.
Villien, M., Bouzat, P., Rupp, T., Robach, P., Lamalle, L., Tropres, I., Esteve, F., Krainik, A.,
Levy, P., Warnking, J.M., Verges, S., 2013. Changes in cerebral blood flow and vasoreactivity to
C02 measured by arterial spin labeling after 6days at 4350m. Neuroimage 72, 272-279.
130
Vlassenko, A.G., Vaishnavi, S.N., Couture, L., Sacco, D., Shannon, B.J., Mach, R.H., Morris,
J.C., Raichle, M.E., Mintun, M.A., 2010. Spatial correlation between brain aerobic glycolysis and
amyloid-beta (Abeta ) deposition. Proc Natl Acad Sci U S A 107, 17763-17767.
Vock, P., Terrier, F., Wegmuller, H., Mahler, F., Gertsch, P., Souza, S.P., Dumoulin, C.L., 1991.
Magnetic resonance angiography of abdominal vessels: early experience using the threedimensional phase-contrast technique. Br J Radiol 64, 10-16.
von Pawel, J., von Roemeling, R., Gatzemeier, U., Boyer, M., Elisson, L.O., Clark, P., Talbot,
D., Rey, A., Butler, T.W., Hirsh, V., Olver, I., Bergman, B., Ayoub, J., Richardson, G., Dunlop,
D., Arcenas, A., Vescio, R., Viallet, J., Treat, J., 2000. Tirapazamine plus cisplatin versus
cisplatin in advanced non-small-cell lung cancer: A report of the international CATAPULT I study
group. Cisplatin and Tirapazamine in Subjects with Advanced Previously Untreated Non-SmallCell Lung Tumors. J Clin Oncol 18, 1351-1359.
Wang, Y., Yu, Y., Li, D., Bae, K.T., Brown, J.J., Lin, W., Haacke, E.M., 2000a. Artery and vein
separation using susceptibility-dependent phase in contrast-enhanced MRA. Journal of
Magnetic Resonance Imaging 12, 661-670.
Wang, Y., Yu, Y., Li, D., Bae, K.T., Brown, J.J., Lin, W., Haacke, E.M., 2000b. Artery and vein
separation using susceptibility-dependent phase in contrast-enhanced MRA. J Magn Reson
Imaging 12, 661-670.
Warach, S., Al-Rawi, Y., Furlan, A.J., Fiebach, J.B., Wintermark, M., Lindsten, A., Smyej, J.,
Bharucha, D.B., Pedraza, S., Rowley, H.A., 2012. Refinement of the magnetic resonance
diffusion-perfusion mismatch concept for thrombolytic patient selection: insights from the
desmoteplase in acute stroke trials. Stroke 43, 2313-2318.
Warburg, 0., 1956. On the origin of cancer cells. Science 123, 309-314.
Wardlaw, J.M., von Heijne, A., 2006. Increased oxygen extraction demonstrated on gradient
echo (T2*) imaging in a patient with acute ischaemic stroke. Cerebrovasc Dis 22, 456-458.
Weisskoff, R.M., Kiihne, S., 1992a. Mri Susceptometry - Image-Based Measurement of
Absolute Susceptibility of Mr Contrast Agents and Human Blood. Magnetic Resonance in
Medicine 24, 375-383.
Weisskoff, R.M., Kiihne, S., 1992b. MRI susceptometry: image-based measurement of absolute
susceptibility of MR contrast agents and human blood. Magn Reson Med 24, 375-383.
Wharton, S., Schafer, A., Bowtell, R., 2010. Susceptibility Mapping in the Human Brain Using
Threshold-Based k-Space Division. Magnetic Resonance in Medicine 63, 1292-1304.
Wise, R.J., Rhodes, C.G., Gibbs, J.M., Hatazawa, J., Palmer, T., Frackowiak, R.S., Jones, T.,
1983. Disturbance of oxidative metabolism of glucose in recent human cerebral infarcts. Ann
Neurol 14, 627-637.
Witte, M.E., Bo, L., Rodenburg, R.J., Belien, J.A., Musters, R., Hazes, T., Wintjes, L.T.,
Smeitink, J.A., Geurts, J.J., De Vries, H.E., van der Valk, P., van Horssen, J., 2009. Enhanced
number and activity of mitochondria in multiple sclerosis lesions. J Pathol 219, 193-204.
131
Wong, E., Guo, J., 2013a. VENTI: Venous Territory Imaging using Remote Sensing. Proc Int
Soc Magn Reson Med, Salt Lake City, Utah, USA, p. 103.
Wong, E.C., Guo, J., 2013b. VENTI: Venous Territory Imaging using Remote Sensing., Proc Int
Soc Magn Reson Med, Salt Lake City, UT, p. 103.
Wright, G.A., Hu, B.S., Macovski, A., 1991. 1991 1.1. Rabi Award. Estimating oxygen saturation
of blood in vivo with MR imaging at 1.5 T. J Magn Reson Imaging 1, 275-283.
Wu, B., Li, W., Avram, A.V., Gho, S.M., Liu, C., 2012a. Fast and tissue-optimized mapping of
magnetic susceptibility and T2* with multi-echo and multi-shot spirals. Neuroimage 59, 297-305.
Wu, B., Li, W., Guidon, A., Liu, C., 2012b. Whole brain susceptibility mapping using compressed
sensing. Magn Reson Med 67, 137-147.
Wu, W.C., Fernandez-Seara, M., Detre, J.A., Wehrli, F.W., Wang, J., 2007. A theoretical and
experimental investigation of the tagging efficiency of pseudocontinuous arterial spin labeling.
Magn Reson Med 58, 1020-1027.
Xu, B., Liu, T., Spincemaille, P., Prince, M., Wang, Y., 2013. Flow compensated quantitative
susceptibility mapping for venous oxygenation imaging. Magn Reson Med.
Xu, F., Uh, J., Brier, M.R., Hart, J., Jr., Yezhuvath, U.S., Gu, H., Yang, Y., Lu, H., 2011. The
influence of carbon dioxide on brain activity and metabolism in conscious humans. J Cereb
Blood Flow Metab 31, 58-67.
Xu, F., Uh, J., Liu, P., Lu, H., 2012. On improving the speed and reliability of T2-relaxationunder-spin-tagging (TRUST) MRI. Magn Reson Med 68, 198-204.
Xu, G., Rowley, H.A., Wu, G., Alsop, D.C., Shankaranarayanan, A., Dowling, M., Christian, B.T.,
Oakes, T.R., Johnson, S.C., 2010. Reliability and precision of pseudo-continuous arterial spin
labeling perfusion MRI on 3.0 T and comparison with 150-water PET in elderly subjects at risk
for Alzheimer's disease. NMR Biomed 23, 286-293.
Yablonskiy, D.A., 1998. Quantitation of intrinsic magnetic susceptibility-related effects in a tissue
matrix. Phantom study. Magn Reson Med 39, 417-428.
Yablonskiy, D.A., Haacke, E.M., 1994. Theory of NMR signal behavior in magnetically
inhomogeneous tissues: the static dephasing regime. Magn Reson Med 32, 749-763.
Yamaguchi, T., Kanno, I., Uemura, K., Shishido, F., Inugami, A., Ogawa, T., Murakami, M.,
Suzuki, K., 1986. Reduction in regional cerebral metabolic rate of oxygen during human aging.
Stroke 17, 1220-1228.
Yamauchi, H., Fukuyama, H., Ogawa, M., Ouchi, Y., Kimura, J., 1994. Callosal atrophy in
patients with lacunar infarction and extensive leukoaraiosis. An indicator of cognitive
impairment. Stroke 25, 1788-1793.
Yang, S.P., Krasney, J.A., 1995. Cerebral blood flow and metabolic responses to sustained
hypercapnia in awake sheep. J Cereb Blood Flow Metab 15, 115-123.
132
Yao, B., Li, T.Q., van Gelderen, P., Shmueli, K., de Zwart, J.A., Duyn, J.H., 2009. Susceptibility
contrast in high field MRI of human brain as a function of tissue iron content. Neuroimage 44,
1259-1266.
Young, W.K., Vojnovic, B., Wardman, P., 1996. Measurement of oxygen tension in tumours by
time-resolved fluorescence. Br J Cancer Suppl 27, S256-259.
Zappe, A.C., Uludag, K., Oeltermann, A., Ugurbil, K., Logothetis, N.K., 2008. The influence of
moderate hypercapnia on neural activity in the anesthetized nonhuman primate. Cereb Cortex
18, 2666-2673.
Zaro-Weber, 0., Moeller-Hartmann, W., Heiss, W.D., Sobesky, J., 2010a. MRI perfusion maps
in acute stroke validated with 150-water positron emission tomography. Stroke 41, 443-449.
Zaro-Weber, 0., Moeller-Hartmann, W., Heiss, W.D., Sobesky, J., 2010b. A simple positron
emission tomography-based calibration for perfusion-weighted magnetic resonance maps to
optimize penumbral flow detection in acute stroke. Stroke 41, 1939-1945.
Zhang, J., Chen, Y.M., Zhang, Y.T., 2011. Blood-oxygenation-level-dependent-(BOLD-) based
R2' MRI study in monkey model of reversible middle cerebral artery occlusion. J Biomed
Biotechnol 2011, 318346.
Zhao, J.M., Clingman, C.S., Narvainen, M.J., Kauppinen, R.A., van Zijl, P.C., 2007.
Oxygenation and hematocrit dependence of transverse relaxation rates of blood at 3T. Magn
Reson Med 58, 592-597.
Zheng, J., An, H., Coggan, A.R., Zhang, X., Bashir, A., Muccigrosso, D., Peterson, L.R.,
Gropler, R.J., 2014. Noncontrast skeletal muscle oximetry. Magn Reson Med 71, 318-325.
133