Diffuse Optical Imaging of Brain
Activation
David Boas
photon migration imaging lab
http://www.nmr.mgh.harvard.edu/DOT/
Andy Siegel
Maria Angela
.
Franceschini
Juliette Selb
Ted Huppert
Danny Joseph
Jon Stott
Sol Diamond
Rick Hoge
Anna Custo
Yiheng Zhang
Gary Strangman
Anders Dale
2
cm
1.5
EEG/MEG
PET/SPECT
1
Diffuse
Optical
0.5
mm
Spatial Sensitivity (log mm)
m
Why Optical Imaging of the Brain?
fMRI
0
-3
-2
millisecond
-1
0
second
1
2
3
hour
4
day
Temporal Sensitivity (log sec)
But also NOVEL CONTRAST that spans all other methods!
Intrinsic Optical Contrast
Parameters
Hemodynamic
•Oxy- & DeoxyHemoglobin
•Blood Volume
•Blood Flow
Neuronal - Electrical
•Scattering
Metabolic
•Cytochrome
oxidase
•CMRO2
Outline
• Basics of Diffuse Optical Imaging
Forward and Inverse Problem
• Examples of Hemodynamic and Fast Imaging
• Improving spatial uniformity, CNR, depth sensitivity
• Multi-modality – Optical and MRI but also MEG/EEG
CMRO2
temporal and spatial correlation
Synergy
Penetrating Deep Tissues
la ~ 100 mm
ls ~ 1 mm
Absorption
Scattering
Radiative Transport Equation


 ˆ
 ˆ ˆ
 ˆ
 ˆ
1 L r , 
,t
ˆ 
ˆ  d
ˆ   S r, 
ˆ ,t
   L r,
, t   t L r , 
, t  s  L r , 
,t f 
v
t








FLUENCE

 ˆ
ˆ
r , t    dL r , , t


 
 ˆ ˆ
ˆ
J r , t    dL r , , t 

FLUX

 ˆ

1
3  
L r , , t 
r , t  
J r , t 
4
4
DIFFUSION APPROXIMATION



Photon Diffuse Equation


2
Time Domain:  D  v a    r, t   vSr, t 
t 

Frequency:
 2  k 2  r    v S r 


D
 v a  i
k 
D
2
Photon Migration in the Head
Source
Photon Migration in the Head
(a)
(b)
layer
a (cm-1) s’ (cm-1)
scalp
0.191
6.6
skull
0.136
8.6
csf
0.026
0.1
gray
matter
0.186
11.1
white
matter
0.186
11.1
Comparison of X-Ray CT and DOT
DOT
Image
Sensitivity Profile
X-Ray
Linear Image Reconstruction
 PERT rs , rd    d 3 r INC rs , r r G r, rd 
y  Ax
{
yi   PERT rs ,i , rd ,i 
Ai , j   INC rs ,i , r j G r j , rd ,i d 3r
x j  r j 
T
1 T
~
Least Square’s Solution: x  ( A A) A y
Under-determined, ill-posed,
ill-conditioned  REGULARIZATION
Spectroscopic Determination of
Oxy- & Deoxy-Hemoglobin
 a     HbO ( )[ HbO 2 ]   HbR ( )[ HbR ]
2
0  a (1 ) 
 1 (1 )  A(1 )
 ( )   0
  ( )
A
(

)
a
2 
2 
 1 2  
Φ1  Ax  n
W  RA T ( ARA T  C) 1
xˆ  WΦ m
 [ HbO 2 ]
 AE 


[
HbR
]


  HbO2 (1 )A(1 )  HbR1 (1 )A(1 )  HbO2 




(

)
A
(

)

(

)
A
(

)



HbR
2
HbR
2
2 

 HbO2 2
INSTRUMENTATION
CW, RF, and Time Domain
Advantage
Disadvan.
Time
Frequency
Time Impulse Response
Expensive
Complex
Physicist
Freq
Phase & Amp
Fast
Cheaper
RF elec.
CW
Cheapest
Easy electronics
Fast
Amp only
instrument development - CW
I0
It
Techen Inc., Milford, MA, http://www.nirsoptix.com
instrument development - CW
32 laser diode sources (690 &
830nm) frequency encoded in
200Hz steps between 4.0kHz and
7.4kHz
32 parallel APD detectors
detector’s output is digitized at
~40kHz on-line, individual source
signals obtained off-line by
infinite-impulse-response filters
acquisition time per image (32x32
channels) can be as short as
10ms!!!
Techen Inc., Milford, MA, http://www.nirsoptix.com
instrument development - TD
I0
t=0
It
~ns
light source: mode-locked Ti:Sapphire laser
(Spectra Physics MaiTai, <100fs pulse width)
tunable from 750–850nm
light detector: temporally gated image
intensified CCD detector (LaVision) which
simultaneously image light from up to 315
detector fibers
the laser delivers light to up to 150 different
source
positions,
with
a
dual-axis
galvanometer that switches between any two
fiber outputs in ~1ms
the gated image intensifier acts as a fast
(200–1000ps) shutter passing only a “window”
of the emerging light back to the CCD
ART, Montreal Canada, http://www.art.ca
probe development
experimental setup
grass stimulator
CW4
optical probe
motion sensors
strain gauge belt
pulse oximeter on a toe
Data Analysis Tools
2
1
1
3
5
5
4
17
9
18
10
7
6
11
3
4
6
8
10
2
8
19
20
21
9
24
12
22
12
26
13
29
14
30
7
14
15
31
16
32
11
23
25
27
13
28
HOMER
filters
raw data
stimuli onsets
probe geometry
block average of the
active stimuli
Data analysis - Homer©
HbO and HbR time traces
Imaging
average of multiple trials
hemodynamic evoked response
of the sensorimotor cortex
during active and passive tasks
 passive stimuli give a weaker and smaller activation
 passive stimuli are more controlled and less prone
to motion artifacts
 the study demonstrates the capability of DOI to
detect the hemodynamic evoked responses to voluntary
and non-voluntary stimuli in the sensorimotor cortex
M. A. Franceschini et al., Psychophysiology 40, 548 (2003)
Motor-Sensory Stimuli
electrical median nerve
finger tactile
oxy maps
finger opposition
2
9
14
4
15
6
10 11
5
6
1
3
1
detectors
sources (690 & 830 nm)
8
12
7
8
5
1.9 cm
x
2
16
-0.80
0.80
0.0
HbO (M)
-0.50
0.0
HbO (M)
0.50
-0.40
0.0
Hb (M)
0.40
-0.25
0.0
Hb (M)
0.25
-0.25
0.0
HbO (M)
0.25
0.0
Hb (M)
0.12
7
3 cm
3
4
deoxy maps
13
Franceschini et al, Psychophysiology, 40:548 (2003).
-0.12
Motor-Sensory Stimuli
hemoglobin changes (M)
right hand finger opposition
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
right hand tactile
0.2
0.2
0.1
0.1
(a)
0
0
-0.1
-0.2
left hand finger opposition
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-10
right hand electrical
0.3
0.3
-0.1
left hand tactile
0.2
left hand electrical
0.2
0.1
0.1
(b)
0
0
-0.1
0
10
20
time (s)
30
40
-0.2
-10
0
10
20
time (s)
HbO contralateral
HbO ipsilateral
30
40
-0.1
-10
0
10
20
time (s)
Hb contralateral
Hb ipsilateral
30
40
FAST SCATTERING SIGNAL
noninvasive optical measurement
of direct neural activation
(time scale of milliseconds)
How?
M.A. Franceschini and D.A. Boas, NeuroImage, 2004
event-related optical signal
G. Gratton et al. (1995) Shades of gray matter: non-invasive optical images of
human brain responses during visual stimulation. Psychophysiology 32, 505–509
Event-related optical signal (EROS) recorded from medial occipital cortical area
during a visual stimulation paradigm. (b) EROS effect. Filled circles refer to the
EROS activity (average of three subjects) recorded from predicted locations for
each quadrant stimulation condition, and open circles to the EROS activity
recorded from the same locations when the opposite quadrant is stimulated.
we want to measure the fast signal with CW
MonteCarlo simulation of light
propagation through the head
absorbing perturbation
scattering perturbation
logarithmic scale
blue = decrease in intensity
red= increase in intensity
scattering
perturbation
0
5
I/I0 (%)
-5
0
0
-0.05
-0.005
-0.1
-0.01
-0.15
-0.015
-0.2
-0.02
-1
0
1
2
3
4
5
pixels
1 pixel = 2 mm
a scattering perturbation in the cortex that causes a 0.1%
change in intensity  will cause a phase shift of 0.008 deg
(@ a modulation frequency of 100 MHz)
phase shift (deg)
intensity vs. phase
filtering out the heart
1
0
-1
-2
0
50
2
I/I0 (%)
I/I0 (%)
2
100
time (sec)
150
1
0
-1
-2
50
7099
90
110
130
70
80
9090
110
120
130
60
70
80
110
120
130
140
90
94
96
98
102
104
106
108 150
110
98
80
90
101
110
102
120
93
95
97
99 100
103
105
107
96 92
98
100101
102
104
time (sec)
200
Filtering out the heart
(b)
power spectra
relative signal change
(a)
time (sec)
raw data
heart fit
frequency (Hz)
subtraction of the two signals
Apparent Fast Signal
I/I0 (%)
0.02
raw data
odd filtered
0.01
0.00
-0.01
even filtered
heart filtered data
-0.02
-0.1
0
0.1
0.2
time (sec)
0.3
0.4
criteria to assess the fast signal
electrical median nerve stimulation ~4.0 Hz
0.06
0.06
0.04
0.04
830 nm
0.02
I/I0 (%)
I/I0 (%)
finger tapping self-paced~3.2 Hz
0.00
-0.02
-0.04
0
I/I0 (%)
0.1
0.2
time (sec)
0.3
0.4
-0.02
-0.1
tactile stimulation ~3.9 Hz
0.03
stim. on
0.1
0.2
time (sec)
0.3
0.4
0.020
0.015
odd stim.
0.01
0
electrical median nerve stimulation ~3.7 Hz
all the stim.
0.02
0.00
ipsilateral hand
0.010
0.005
0.000
-0.005
-0.01
-0.02
-0.010
even stim.
-0.03
contralateral hand
-0.015
-0.020
-0.04
-0.1
0.00
-0.06
I/I0 (%)
0.04
0.02
-0.04
690 nm
-0.06
-0.1
stim. off
0
0.1
0.2
time (sec)
0.3
0.4
-0.1
0
0.1
0.2
time (sec)
0.3
0.4
0.04
0.04
0.02
0.02
830 nm
0.00
I/I0 (%)
I/I0 (%)
fast signal during finger tapping
-0.02
-0.04
690 nm
-0.06
0.00
-0.02
-0.04
-0.06
stim. on
-0.08
-0.08
-0.1
stim. off
0
0.1
0.2
0.3
-0.1
0.4
0
time (sec)
0.04
0.02
odd stim.
-0.02
-0.04
-0.06
-0.1
0
0.1
0.2
time (sec)
0.4
0.3
ipsilateral hand
0.00
-0.02
-0.04
-0.06
even stim.
-0.08
0.3
0.04
I/I0 (%)
I/I0 (%)
0.00
0.2
time (sec)
all the stim.
0.02
0.1
contralateral hand
-0.08
0.4
-0.1
0
0.1
0.2
time (sec)
0.3
0.4
5 lhtap
localization
tactile stimulation
contral.
0
25
50
75
100
125
0
0.1
150
175
200
225 ms
RH
LH
contral.
-0.1
I/I0 @ 830nm (%)
5 pas
Scattering or Absorption Contrast
absorbing perturbation
scattering perturbation
(c)
16
15
6
14
4
13
2
12 8
11 7
10 6
7
5
3
1
1.9cm
8
3cm
(b)
4
3
2
logarithmic scale
1
9 5
detectors
sources (690 & 830nm)
overlapping measurements to
improve fast signal detection
overlapping measurements increase spatial uniformity
improving detection rate!!!
1st neighbor overlapping
scattering inclusion 1.5cm deep
with 1st neighbor measurements spatial uniformity is poor
and the detection of the fast signal strongly depends on
the position of the scattering change relative to sources
and detectors
Current Imaging Issues
• Spatial uniformity – More dense, overlapping measurements
• Physiological signal clutter – cardiac, respiration, blood pressure
PCA, ICA, physiological modeling
• Depth penetration and sensitivity
signal processing to filter
physiological clutter from brain
activation signals
our raw optical data show ~1Hz cardiac pulsation, ~0.1-0.3
Hz blood pressure and respiration fluctuations
optical signal
OD
blood pressure
respiration
time (sec)
cross-correlation of the optical data with
respiration
blood pressure
cross-correlation coefficient
heart beat
time (sec)
a random optode in the head
spatial-temporal covariance
2
1
1
3
5
5
OD
3
4
9
18
10
7
19
20
21
6
11
17
4
6
8
10
2
8
9
24
12
22
12
26
13
29
14
30
7
14
15
31
16
11
32
time (sec)
23
25
27
13
28
cross-correlation of the optical data with
heart beat
-0.1
0
×0.5
respiration
0.1
-0.2
0
×1
blood pressure
0.2
10 frames per second
-0.7
0
0.7
×5
we want to use these maps to spatially filter systemic signal clutter
spatial-temporal map of brain activation
OD @ 830
nm during
finger tapping:
10 sec stim
and 20 sec
rest
2
1
1
3
5
5
2
4
10
17
10
20
24
12
13
29
30
7
30
14
15
31
16
32
11
22
12
26
14
0
-5
19
21
9
7
9
18
7
6
11
3
4
6
8
8
raw data with temporal
filtering – data band
pass filtered between
0.02 and 0.8 Hz
23
25
27
13
28
PCA using baseline spatio-temporal info
we used a
principle
component
analysis on
baseline data
to identify
the major
component of
spatial
covariance
and projected
out this
spatial
component on
the stimulus
data
raw data
raw data
PCA filter
PCA
cross-correlation optical signal and
physiology vs. PCA maps
I principal component
II principal component
CC with blood pressure
CC with respiration
Blood Pressure CC~0.5
Respiration CC~0.1
Systemic physiology has strong
fluctuations in the scalp
Our CW optical
measurements are
most sensitive to the
scalp
Can we reduce sensitivity to the scalp?
Increasing Depth Penetration
and Depth Resolution
CW
1 ns
2 ns
Time Domain measurements will give us:
1) Better depth resolution
2) Better depth penetration (CNR vs CBR)
TD measurement of brain activity
3 cm
3 cm
7 Hz
acquisition
2 cm
CNR
19 to 25
1 cm
CBR
8.9 to 6.8
Time (ns)
DEPTH DISCRIMINATION WILL FURTHER INCREASE CBR!
0ns
0.5ns
1ns
1.5ns
Time (s)
2ns
2.5ns
3ns
CW
Multi-Modality Imaging
Why? – combine complimentary information
Why optical? – unique functional information but inferior
structural information
Why combine DOT and MRI / X-Ray / US?
1. Benefit from MRI/X-Ray spatial and Optical spectroscopy - Structure
and Function
2. Cerebral Metabolic Rate of Oxygen (CMRO2), tumor oxygen
consumption
Clinical utility of combine DOT and MRI / X-Ray / US?
1. Stroke: MRI (T1,DWI,PWI,BOLD) – DOT adds quant hemo and CMRO2
2. Neuro-Degenerative Brain Diseases – DOT brings more functional info
3. Breast – DOT adds functional overlay to X-Ray structural screening &
diagnosis
4. Breast – with MRI could constrain DOT and determine tumor oxygen
consumption
Calculation of CMRO2
CMRO 2  CBF  SaO 2  SvO 2 
 CBF  OEF  SaO 2
Assume SaO2=1
 CMRO 2   CBF 
HbR 
HbT 
1 
  1 





1


1


R
T





 CMRO 
HbR o 
HbTo 
2,O 
 CBFO 

ASL
[HbO2]V
BOLD
[HbO2]A
O2
Optical
1
NIRS and fMRI of CMRO2
Hoge, et al., Presented at the meeting of Human Brain Mapping 2003., submitted
Simultaneous DOT and fMRI
785nm
830nm
BOLD
A
D
Strangman, et al. NeuroImage 17: 719-731. 2002.
temporal comparison BOLD & DOT
12.5
1.0
HbO
HbO
HbT
HbT
[HbR] [HbR]
0.100
10
[HbT] [HbT]
BOLD
BOLD
8
10.0
0.080
6
7.5
0.060
0.6
5.0
0.040
BOLD
0.4
2.5
4
2
0.020
BOLD
0.0
0.2
0
0.000
HbR
HbR
-2.5
0.0
0
1
2
3
4
5
5
66
-0.020
-2
77
88
99
time (sec)
Time
(sec)
Time(sec)
10 11
11 12
12 13
13 14
14 15
15
10
 BOLD (%)
0.8
BOLD (%)
Normalized Change (AU)
(au)
change
normalized
(M)
hemoglobin
Concentration(
M)
hemodynamic evoked response measured with DOT and fMRI
during event-related finger tapping[HbO]
stimulation
[HbO]
temporal comparison BOLD & DOT
change (au)
normalized
Normalized Change (AU)
hemodynamic evoked response measured with DOT and fMRI
during event-related finger tapping stimulation
Normalized 1/-BOLD (AU)
HbT
0.8
0.6
BOLD
0.4
0.2
0.0
0
HbR:(1/BOLD)
1
2
3
4
Hb
R6
5
7
8
time
(sec)
9
10
11
12
13
14
15
HbT:(1/BOLD)
1.2
Time(sec)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.2
HbO
1.0
1.0
Normalized 1/BOLD (AU)
1.2
[HbO]
[HbR]
[HbT]
BOLD
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
Normalized -HbR (AU)
0.8
1.0
1.2
-0.2
-0.2
0.0
0.2
0.4
0.6
0.8
Normalized -HbT (AU)
1.0
1.2
temporal comparison ASL & DOT
HbR:ASL
1.2
HbT:ASL
1.0
1.0
0.8
0.6
0.6
0.4
ASL
0.2
0.0
-0.2
Normalized ASL (AU)
Normalized ASL (AU)
0.8
0.4
0.2
0.0
-0.2
-0.4
-0.4
-0.6
-0.6
-0.8
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-0.8
-0.8
1.2
-0.6
-0.4
Normalized HbR (AU)
1.2
HbR:(1/BOLD)
0.2
0.4
0.6
0.8
1.0
HbT:(1/BOLD)
1.2
1.0
0.8
BOLD
0.6
0.4
0.2
0.0
Normalized 1/BOLD (AU)
Normalized 1/-BOLD (AU)
0.0
Normalized HbT (AU)
1.0
-0.2
-0.2
-0.2
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
Normalized -HbR (AU)
0.8
1.0
1.2
-0.2
-0.2
0.0
0.2
0.4
0.6
0.8
Normalized -HbT (AU)
1.0
1.2
spatial correlation DOT-fMRI
5
8
4
7
3
6
2
1
4
3
2
BOLD image projected on
the surface
of the head
8
3
6
5
2
4
7
1
6
3
4
3
2
2
1
5
2
1
8
2
4
7
6
5
4
3
3
2
1
2
1
DOT
MRI Tissue Segmentation to guide
DOT
Tissue Parameter Estimation Using
Multi-Spectral 3-D Protocol
o
FA = 3
o
FA = 5
MRI Forward Model (FLASH/SSC):
o
FA = 30
1  e TR / T1
TE / T2
I (TR, TE, , T1 , T2 , P)  P sin  (
)
e
TR / T1
1  cos e
Dale and Fischl
Inferring Tissue
Optical
Properties
Infer Homogeneous Medium
• Bayesian inference
• Measurement and model noise
• 8 Temporal Point Spread Functions
Infer Segmented Medium
Barnett et al, Applied Optics, 42:3095 (2003).
cortically constrained DOT of brain activation
localization of true
simulated activation
relative to optodes
1st neighbor planar
reconstruction
1st & 2nd neighbor
planar reconstruction
coronal slice of the
1st neighbor reconstruction
1st & 2nd neighbor
anatomical MRI segmented
with anatomical cortical
reconstruction with
human head
constrain
anatomical cortical constrain
Summary
• NIRS Imaging of Brain Function is rapidly evolving to
improve spatial resolution and depth penetration
• Multi-modality integration enables quantitative imaging and
estimate of CMRO2
• Explore Neuro-Metabolic-Vascular relationship
• Neuroscience and Clinical Applications
photon migration imaging lab
http://www.nmr.mgh.harvard.edu/DOT/
Andy Siegel
Maria Angela
Franceschini
Juliette Selb
Ted Huppert
Danny Joseph
Jon Stott
Sol Diamond
Rick Hoge
Anna Custo
Yiheng Zhang
Gary Strangman
Anders Dale