Whole Brain Myelin Imaging with
mcDESPOT in Multiple Sclerosis
July 18, 2012
Jason Su
Outline
1.
2.
3.
4.
5.
Introduction to parametric mapping
Myelin imaging and MWF
mcDESPOT measurement of 2-pool exchange
mcDESPOT in multiple sclerosis
Current and future challenges
What is Parametric Mapping?
1. Start with a signal model for your data
2. Collect a series of scans, typically with only 1
or 2 sequence variables changing
3. Fit model to data
• Motivation
– Reveals quantifiable physical properties of tissue
unlike conventional imaging
– Maps are ideally scanner independent
Parametric Mapping
• Some examples
– FA/MD mapping with DTI – most widely known
mapping sequence
– T1 mapping – relevant in study of contrast agent
relaxivity and diseases
– B1 mapping – important for high field applications
T1 Mapping Motivation
T1 mapping in multiple sclerosis
DCE-MRI in
tumors:
[Gd] related to T1
grade II
grade IV
grade III
Levesque et al. 2010
Tofts et al. 1999, 2003, Patankar et al. 2005
Relaxation Mapping
• T1 mapping
– IR SE – gold standard, vary TI
– Look-Locker – use multiple readout pulses to collect
many TIs
– DESPOT1 – vary flip angle
• T2 mapping
– Dual SE – vary TE
– CPMG – use multiple spin echoes to collect many TEs
– DESPOT2 – vary flip angle
T1 Mapping: Inversion Recovery
S    M 0 1   e
Gowland &Stevenson, in Tofts ed., QMRI of the Brain, 2003
Brix et al. MRI 1990; Ropele et al. MRM 1999; Wang et al. MRM 1987
 TI T1

DESPOT1 T1 mapping
S    M 0 sin  e

Christensen 1974, Homer 1984, Wang 1987, Deoni 2003
 TE
*
T2
1e
 TR T1
1  cos  e
 TR T1
DESPOT Methods
• Vary flip angle in steady state sequences like SPGR and SSFP
• Fast, whole brain, higher resolution 1-2mm isotropic
• Requires accurate knowledge of flip angle
1.
2.
B1+ transmit field inhomogeneity – problem for >1.5T
Excitation slab profile – typically known and accounted for
• DESPOT1 – T1 mapping
– DESPOT-HIFI – add an inversion to allow T1 and B1+ mapping
• DESPOT2 – T1 and T2 mapping
– DESPOT-FM – collect multiple SSFP phase cycles to map B0
• mcDESPOT – multi-component T1 and T2 mapping
Relaxation Based Myelin
Imaging
• DTI is not an ideal
measure of myelin (low
resolution, crossing fibers
problem)
• T2 (or R2) has been used
in the past as a crude
correlate of myelin
– Myelination reduces water
content in brain, lower T2
– T2w FLAIR is used in MS to
highlight lesions
– T2 mapping gives a more
sensitive indicator
Myelin Water Fraction
Recent methods have focused on a more
specific measure: myelin water fraction
(MWF)
• Multiecho qT2 – vary TE, decomposes
the signal into a spectrum of T2 times
(UBC, MacKay)
– Well validated way to produce MWF
maps that represent myelin
– Few slices, long acquisition time
• Intra- and extra-cellular water, T2 ≈
80ms
• Myelin water, T2 ≈ 20ms
• mcDESPOT – vary flip angle, models
SPGR and SSFP steady state signal
– Also based on modeling relaxation and
two pool exchange
– Validation in progress
– High resolution, whole brain, but long
processing time (24 hours)
FA vs MWF
Fractional Anisotropy map (3T), MWF (qT2, 3T)
MWF (1.5T, mcDESPOT)
mcDESPOT
• Models tissue as two water pools in exchange
– Fast relaxing water pool
– Slow relaxing water pool
𝑓𝐹 + 𝑓𝑆 = 1
T1,F T2,F
fF
kFS
T1,S T2,S
fS
kSF
• Assume chemical equilibrium:
𝑓𝐹 𝑘𝐹𝑆 = 𝑓𝑆 𝑘𝑆𝐹
• The SPGR and SSFP signal equations must be
adapted to take into account this model
mcDESPOT Model: SPGR
• SPGR equation
– Single Component
𝑀0 1 − 𝐸1 sin(𝛼)
𝑆𝑆𝑃𝐺𝑅 =
1 − 𝐸1 cos(𝛼)
𝐸1 = e
−
𝑇𝑅
𝑇1
mcDESPOT Model: SPGR
• SPGR Equation
– Multi-Component
𝑆𝑆𝑃𝐺𝑅 = 𝑀0,𝑆𝑃𝐺𝑅 𝐼 − 𝑒 𝐴𝑆𝑃𝐺𝑅𝑇𝑅 sin 𝛼
𝐼 − 𝑒 𝐴𝑆𝑃𝐺𝑅 𝑇𝑅 cos 𝛼 −1
𝑓𝐹
𝑀0,𝑆𝑃𝐺𝑅 = 𝑀0
𝑓𝑆
mcDESPOT Model: SPGR
• Single component fit of multi-component data
Deoni et al. 2008
mcDESPOT Model Fitting
• Expensive non-linear curve fitting problem
– 24 hour per 2mm isotropic brain with 12-core CPU
• Previous implementations used genetic
algorithms
• Currently using stochastic region of
contraction
mcDESPOT Maps in Normal
T1single
T1slow
T1fast
MWF
0 – 2345ms
0 – 1172ms
0 – 555ms
0 – 0.234
0 – 328ms
0 – 123ms
0 – 9.26ms
0 – 137ms
T2single
T2slow
T2fast
Residence Time
ISMRM 2011 E-POSTER #4643
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN
MS PATIENTS COMPARED TO ATROPHY MEASURES ALONE
J.Su1, H.H.Kitzler2, M.Zeineh1, S.C.Deoni3, C.Harper-Little2,
A.Leung2, M.Kremenchutzky2, and B.K.Rutt1
1Stanford
U, CA, USA, 2TU Dresden, SN, Germany, 2U of Western Ontario, ON, Canada, 3Brown U, RI, USA
Declaration of Conflict of Interest or Relationship
I have no conflicts of interest to disclose with regard
to the subject matter of this presentation.
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Background
• Conventional MRI measures such as lesion
load have been criticized with adding little
new information on top of clinical scores for
multiple sclerosis (MS) patients
• Measures that quantify the hidden burden of
disease in white matter are urgently needed
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Purpose
• To apply mcDESPOT, a whole-brain, myelinselective, multi-component relaxometric
imaging method, in a pilot MS study
• Assess if the method can explain differences in
disease course and severity by uncovering the
burden of disease in normal-appearing white
matter (NAWM)
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Study
Healthy
Controls
All
Patients
CIS
RRMS
SPMS
PPMS
N
26
26
10
5
6
5
Mean age, yr
(SD)
42
49
41
48
58
55
(13)
(12)
(12)
(12)
(7)
(7)
10/16
7/19
3/7
0/5
0/6
4/1
14
2
15
28
20
(13)
(2)
(10)
(8)
(12)
3.6
1.7
2.0
6.4
5.6
(2.4)
(0.9)
(1.7)
(1.1)
(1.1)
Demographic Data
Male/Female ratio
Mean disease
duration, yr
(SD)
Mean EDSS score
(SD)
—
—
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Scanning Methods
• 1.5T GE Signa HDx, 8-channel head RF coil
• mcDESPOT: 2mm3 isotropic covering whole brain, about 15
min.
– SPGR: TE/TR = 2.1/6.7ms, α = {3,4,5,6,7,8,11,13,18}°
– bSSFP: TE/TR = 1.8/3.6ms, α = {11,14,20,24,28,34,41,51,67}°
• 2D T2 FLAIR: 0.86 mm2 in-plane and 3mm slice resolution
• 3D T1 IR-SPGR: 1mm3 resolution with pre/post Gd contrast
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Processing Methods: MWF
• Linearly coregister and
brain extract mcDESPOT
SPGR and SSFP images
with FSL1
Myelin Water Fraction
• Find myelin water
fraction maps using the
established mcDESPOT
fitting algorithm2
1FMRIB
Software Library. 2Deoni et al., Magn Reson Med. 2008 Dec;60(6):1372-87
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Processing Methods: Deficient
MWF
• Non-linearly register
mcDESPOT MWF maps to
MNI152 standard space
• Combine normals together
to form mean and standard
deviation MWF volumes
• For each subject, calculate a
z-score ([x – μ]/σ) at every
voxel to determine if it is
significantly deficient, i.e.
MWF < -4σ below the mean
Deficient MWF Voxels
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Processing Methods: WM
• Brain extract MPRAGE images
• Segment white and gray
matter with SPM83
• Filter tissue masks to reduce
noise then manually edit by a
trained neuroradiologist
• Calculate parenchymal volume
fraction (PVF) as WM+GM
divided by the brain mask
volume
3Statistical
Parametric Mapping software package.
FLAIR
WM
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Processing Methods: Lesions &
DAWM
• Non-linearly register T2-FLAIR
images to MNI152 standard
space
• Combine normals together to
form mean and standard
deviation volumes
• Segment lesions as those
voxels with z-score > +4 and
diffusely abnormal white
matter > +2
• Edit masks by a trained
neurologist
DAWM
Lesions
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Processing Methods: NAWM &
DVF
• Segment normal-appearing
white matter (NAWM) as
WM – DAWM – lesions
• Find deficient MWF volume
fraction (DVF)
– Sum the volume of deficient
voxels in each tissue
compartment and normalize
by the compartment’s volume
– # deficient voxels in
compartment * voxel volume
/ compartment volume
Normal-Appearing
White Matter
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Segmentations and DV
FLAIR
WM
NAWM
DAWM
Lesions
MWF
Deficient MWF
Voxels
DV in NAWM
DV in DAWM
DV in Lesions
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Statistical Methods
• Use rank sum tests to compare patient groups to normals along
different measures
• Perform an exhaustive search to find the best multiple linear
regression model for EDSS using Mallows’ Cp4 criterion among 21
possible image-derived predictors:
– PVF
– log-DVF in whole brain, log-DVF in WM, log-DVF in NAWM, log-DVF in
lesions
– log-DV in those four compartments
– mean MWF in those four compartments
– volumes of those four compartments (lesion volume = T2 lesion load)
– volume fractions of those four compartments with respect to the
whole brain mask volume
4Mallows
C. Some comments on Cp. Technometrics. 1973;15(4):661-75.
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
Results: Mean MWF in
Compartments
* p < 0.05
** p < 0.01
*** p < 0.001.
*
** **
** **
*
0.15
**
**
***
***
0.10
***
***
0.05
Le
si
on
M
AW
D
AW
M
0.00
N
• Significance levels:
0.20
W
M
• Testing was also done for
RRMS vs. SPMS and CIS vs.
RRMS, any significant
differences are shown with
a connecting bracket
0.25
Mean MWF
• Dotted line shows mean
MWF in WM for normals.
Rank sum testing was done
for each bar against this
ISMRM 2011 #4643
Compartment
CIS
RRMS
SPMS
PPMS
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Results: DVF in Compartments
• PVF, however, fails to
distinguish CIS and RR
patients from normals
Mean DVF
0.1
***
***
*
***
***
***
** *** ***
*
***
***
***
0.01
***
***
***
**
0.001
Le
si
on
AW
M
D
N
AW
M
0.0001
W
M
• With DVF, all patient
subclasses were
significantly different
from healthy controls
*
Compartment
CIS
Parenchymal
Volume Fraction
• Dotted line shows
deficient MWF volume
fraction in WM for
healthy controls
1
RRMS
0.9
0.8
**
0.7
0.6
***
SPMS
PPMS
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
• Lesion load correlates
poorly with EDSS
• PVF and DVF are stronger
indicators of decline
T2-Lesion Load (mm3)
Results: Correlations with EDSS
40,000
R2 = 0.2752
p = 0.0059
30,000
20,000
10,000
0
0
2
4
6
8
EDSS
100
DVFnawm (mm3)
0.9
10-1
PVF
0.8
10-2
0.7
R2 = 0.5617
p < 0.0001
0.6
0.5
0
2
4
EDSS
6
R2 = 0.3734
p < 0.001
10-3
8
10-4
0
2
4
EDSS
6
8
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
Results: Multiple Linear
Regression
ISMRM 2011 #4643
• The best linear model for EDSS contains PVF (p <
0.001), mean MWF in whole brain (p < 0.001),
and WM volume fraction (p < 0.01)
• Whole-brain MWF and WM volume fraction
significantly improve the prediction of EDSS over
that produced by PVF alone
• Explains 76% of the variance in EDSS (R2 = 0.76,
adjusted R2 = 0.73) compared to 56% with only
PVF
MCDESPOT-DERIVED MWF IMPROVES EDSS PREDICTION IN MS PATIENTS COMPARED TO ATROPHY MEASURES
ALONE
ISMRM 2011 #4643
Discussion & Conclusions
• DVF is able to differentiate CIS and RRMS patients from
normals, whereas other measures such as PVF and mean
MWF cannot
• The invisible burden of disease may be more important
than lesions in determining disability, since we observe a
higher correlation of EDSS with DVF in NAWM than lesion
load
• A combination of established atrophy measures with new
mcDESPOT-derived MWF are more capable in accurately
estimating disability than either quantity alone
ISMRM 2011 E-POSTER #7224
SENSITIVE DETECTION OF MYELINATION CHANGE IN
MULTIPLE SCLEROSIS BY MCDESPOT
J.Su1, H.H.Kitzler2, M.Zeineh1, S.C.Deoni3, C.Harper-Little2,
A.Leung2, M.Kremenchutzky2, and B.K.Rutt1
1Stanford
U, CA, USA, 2TU Dresden, SN, Germany, 2U of Western Ontario, ON, Canada, 3Brown U, RI, USA
Declaration of Conflict of Interest or Relationship
I have no conflicts of interest to disclose with regard
to the subject matter of this presentation.
SENSITIVE DETECTION OF MYELINATION CHANGE IN MULTIPLE SCLEROSIS BY MCDESPOT
ISMRM 2011 #7224
Results: Mean MWF in Whole
Brain
-0.02
-0.04
S
S
PP
M
M
R
SP
M
S
-0.06
R
– * p < 0.05
– ** p < 0.01
– *** p < 0.001.
0.00
IS
• Significance levels:
*
C
• Testing was also done for
RRMS vs. SPMS and CIS vs.
RRMS, any significant
differences are shown with a
connecting bracket
0.02
Mean MWF Change
• Dotted line shows mean MWF
for normals. Rank sum testing
was done for each bar against
this value
Whole Brain
SENSITIVE DETECTION OF MYELINATION CHANGE IN MULTIPLE SCLEROSIS BY MCDESPOT
ISMRM 2011 #7224
Results: DVF Change
• Colors denote subject
type
PPMS
• Arrowheads indicate
the direction of change
and the DVF at 1-year
• Dashed lines show
subjects who also had
a change in EDSS
SPMS
RRMS
CIS
Normals
SENSITIVE DETECTION OF MYELINATION CHANGE IN MULTIPLE SCLEROSIS BY MCDESPOT
ISMRM 2011 #7224
Results: DVF in Whole Brain
0.015
0.010
*
0.005
S
PP
M
S
SP
M
M
S
0.000
R
R
• Progressive patients have
a greater rate of DVF
increase
*
C
IS
• Definite MS patients are
losing significantly more
myelin than normals
**
0.020
Mean DVF Change
• Dotted line shows mean
deficient MWF volume
fraction change for
normals
Whole Brain
SENSITIVE DETECTION OF MYELINATION CHANGE IN MULTIPLE SCLEROSIS BY MCDESPOT
ISMRM 2011 #7224
Discussion & Conclusions
• DVF shows statistically significant changes in
brain myelination over the study period
• Progressive patients show greater disease decline
that are not reflected in their EDSS disability
score
• EDSS and DVF appear to measure different
aspects of the disease.
– Patients with changes in EDSS did not actually have
the largest DVF changes
Current and Future Work
• High-Field mcDESPOT
– 3T: 6 min acq. @ 2mm isotropic, post-correction with a B1+ map
is sufficient
– 7T: k-T points pulse design is showing promise in flattening the
transmitted field
• Accelerated mcDESPOT
– DISCO-based view-sharing working with DESPOT1
• SSFP (DESPOT2) more challenging
• Possible new applications
– Alzheimer’s Disease: the myelin hypothesis
– Traumatic brain injury
– Novel segmentation