Introduction to Diffusion MRI
processing
1
The diffusion process
http://pubs.niaaa.nih.gov/publications/arh27-2/146-152.htm
2
dt_recon
• Required Arguments:
• --i invol
• --s subjectid
• --o outputdir
• Example: dt_recon --i
dt_recon --i 6-1025.dcm --s M111 --o dti
3
Main processing steps
• # Eddy current and motion correction
– (FSL eddy_correct)
• # Tensor fitting
– tensor.nii, eigvals.nii. eigvec?.nii
– set of scalar maps: adc, fa, ra, vr, ivc
• # Registration to anatomical space
– (bbregister to lowb)
• # Mapping mask, FA to Talairach space
4
Other Arguments (Optional)
--b bvals bvecs
--info-dump infodump.dat
use info dump created by unpacksdcmdir or dcmunpack
--ecref TP
use TP as 0-based reference time points for EC
--no-ec
turn off eddy/motion correction
--no-reg
do not register to subject or resample to talairach
--no-tal
do not resample FA to talairch space
--sd subjectsdir
specify subjects dir (default env SUBJECTS_DIR)
--eres-save
save resdidual error (dwires and eres)
--pca
run PCA/SVD analysis on eres (saves in pca-eres dir)
--prune_thr thr
set threshold for masking (default is FLT_MIN)
--debug
print out lots of info
--version
print version of this script and exit
--help
voluminous bits of wisdom
5
Examples of scalar maps


• FA: fractional anisotropy (fiber density, axonal
3                

2 
  
diameter, myelination in WM)


• RA: relative anisotropy
var  
• VR: volume ratio
123   3
• IVC: inter-voxel correlation (diffusion orientation
agreement in neighbors)
• ADC: apparent diffusion coefficient (magnitude of
ln S0 S1  b1  b0 
diffusion; low value  organized tracts)
• RD: radial diffusivity
2  3  2
• AD: axial diffusivity
1
• …
2
2
1
2
2
2
1
3
2
2
2
3
6
FA
7
ADC
8
IVC
9
Tractography examples
• Trackvis and Diffusion Toolkit
(http://www.trackvis.org/)
10
11
CST on (color) FA map
12
Under development:
TRActs Constrained by UnderLying
Anatomy (TRACULA)
Anastasia Yendiki
HMS/MGH/MIT Athinoula A. Martinos Center for
Biomedical Imaging
Tractography
• Identify fiber bundles in
cerebral white matter
(WM)
• Characterizing these WM
pathways is important for:
– Inferring connections b/w
brain regions
– Understanding effects of
neurodegenerative diseases,
stroke, aging, development
…
From Gray's Anatomy: IX. Neurology
14/41
Diffusion in brain tissue
• Differentiate tissues based on the diffusion (random
motion) of water molecules within them
• Gray matter: Diffusion is
unrestricted  isotropic
• White matter: Diffusion is
restricted  anisotropic
Diffusion MRI
• Magnetic resonance
imaging can provide
“diffusion encoding”
• Magnetic field strength
is varied by gradients in
different directions
• Image intensity is
attenuated depending
on water diffusion in
each direction
• Compare with baseline
images to infer on
diffusion process
Diffusion
encoding in
direction g1
g2
g3
g4
g5
g6
No diffusion
encoding
16/41
Deterministic vs. probabilistic
• Determine “best” pathway
between two brain regions
• Challenges:
- Noisy, distorted images
- Pathway crossings
- High-dimensional space
• Deterministic methods:
Model geometry of
diffusion data, e.g.,
tensor/eigenvectors [Conturo
‘99, Jones ‘99, Mori ‘99, Basser ‘00, Catani ‘02,
Parker ‘02, O’Donnell ‘02, Lazar ‘03,
Jackowski ‘04, Pichon ‘05, Fletcher ‘07,
Melonakos ‘07, …]
?
• Probabilistic methods:
Also model statistics of
diffusion data [Behrens ‘03,
Hagmann ‘03, Pajevic ‘03, Jones ‘05, Lazar
‘05, Parker ‘05, Friman ‘06, Jbabdi ‘07, …]
17/41
Local vs. global





• Local: Uses local information to determine next step,
errors propagate from areas of high uncertainty
• Global: Integrates information along the entire path
18/41
Local tractography
• Define a “seed” voxel or
ROI to start the tract
from
• Trace the tract by small
steps, determine “best”
direction at each step
• Deterministic: Only
one possible direction
at each step
• Probabilistic: Many possible directions at each step
(because of noise), some more likely than others
19/41
Some issues
• Not constrained to a
connection of the seed
to a target region
• How do we isolate a
specific connection?
We can set a threshold,
but how?
• What if we want a nondominant connection?
We can define
waypoints, but there’s
no guarantee that we’ll
reach them.
• Not symmetric between tract “start” and “end” point
20/41
Global tractography





• Define a “seed” voxel or
ROI
• Define a “target” voxel
or ROI
• Deterministic: Only one
possible path
• Probabilistic: Many
possible paths, find their
probability distribution
• Constrained to a specific connection
• Symmetric between seed and target regions
21/41
Probabilistic tractography
Have set of images
Want most probable path
• Determine the most probable path based on:
– What the images tell us about the path
Assume a multi-compartment model of diffusion [Jbabdi et al.,
NeuroImage ‘07]
– What we already know about the path
Incorporate prior knowledge on path anatomy from training
subjects
22/41
Multi-compartment model
Behrens et al., MRM ‘03
Jbabdi et al., NeuroImage ‘07
1
2
0
• Multiple diffusion compartments in
each voxel:
– Anisotropic compartments that
model fibers (1, 2, …)
– One isotropic compartment that
models everything left over (0)
• We infer from the data:
– Orientation angles of anisotropic compartments
– Volumes of all compartments
– Overall diffusivity in the voxel
• Multiple fibers only if they are supported by data
23/41
Anatomical priors for WM paths
• WM pathways are well-constrained by
surrounding anatomy
• Sources of prior anatomical information:
– Shape of the path in a set of training subjects
– Anatomical regions around the path in the training subjects
• Other types of anatomical constraints often used:
– WM masks
– Constraints on path angle
– Constraints on path length
24/41
TRACULA
• TRActs Constrained by UnderLying Anatomy
• Global probabilistic tractography
• Prior info on tract anatomy from training subjects
– No manual intervention in new subjects
– Robustness w.r.t. initialization and ROI selection
– Anatomically plausible solutions
• Manual labeling of paths on a set of training
subjects, performed by an expert
• Anatomical segmentation maps of
the training subjects, produced by
FreeSurfer
25/41
Preliminary results
Data courtesy of Dr. R. Gollub, MGH
• Manual labeling of:
– Corticospinal tract (CST)
– Superior longitudinal fasciculus (SLF) 1, 2, 3
– Cingulum
• DTI reliability data set from Mental Illness and
Neuroscience Discovery (MIND) Institute
– 10 healthy volunteers scanned twice
– DWI: 2x2x2 mm resolution, 60 gradient directions
– T1: 1x1x1 mm resolution
• Use manual labeling of 9 subjects to obtain path
priors and path initialization for 10th subject
26/41
Reliability study
Manual labeling by Allison Stevens and Cibu Thomas
Visualization tool by Ruopeng Wang
CST
SLF
27/41
Test-retest reliability
No info from training subjects
With info from training subjects
Visit 1
Visit 1
Visit 2
Visit 2
28/41
Application: Huntington’s disease
Data courtesy of Dr. D. Rosas, MGH
Healthy
Huntington’s stage 1
Huntington’s stage 2
Huntington’s stage 3
29/41
MD changes in patients
CST
SLF1
SLF2
SLF3
0.1
Cingulum
0.001
P-values for T-test on mean MD of Huntington’s patients (N=33) and controls (N=22)
30/41
Correlation with disease stage
Left
CST
Right
SLF1 SLF2 SLF3
-.3
CB
FA -.3
-.3
-.3
MD .3
.4
.7
.6
.4
p<10 p<10
-7
-5
-.3
SLF1 SLF2 SLF3
-.5
CB
-.3
-.2
.5
.7
.6
.3
p<10 p<10
-8
-5
-.2
RD .3
.4
.6
.5
.4
.6
.6
.6
.3
AD .3
.4
.7
.6
.4
.4
.8
.5
.3
FA:
Fractional anisotropy
MD: Mean diffusivity
RD:
Radial diffusivity
AD:
Axial diffusivity
CST: Corticospinal tract
SLF: Superior longitudinal fasciculus
CB:
Cingulum body
31/41
Application: Schizophrenia
Data courtesy of Dr. R. Gollub, MGH
CST
SLF1
SLF2
SLF3
Cingulum
0.1
0.001
P-values for T-test on mean RD of schizophrenia patients (N=25) and controls (N=18)
32/41
FA and RD changes
*
*
*
°
*
*
*
°
* p<.05
° p<.10
33/41
Current development
• TRACULA: A method for diffusion tractography that
combines a global probabilistic approach with prior
knowledge on path anatomy
• More detailed models of tracts
• Improved inter-subject registration
• Coming soon to a FreeSurfer near you!
34/41
Acknowledgements
Support provided in part by:
• National Center for Research Resources
– P41 RR14075
– R01 RR16594
– The NCRR BIRN Morphometric Project BIRN002, U24
RR021382
• National Institute for Biomedical Imaging and Bioengineering
– K99 EB008129
– R01 EB001550
– R01 EB006758
• National Institute for Neurological Disorders and Stroke
– R01 NS052585
• Mental Illness and Neuroscience Discovery (MIND) Institute
• National Alliance for Medical Image Computing
– Funded by the NIH Roadmap for Medical Research, grant
U54 EB005149
35/41
Acknowledgements
MGH/Martinos
Lilla Zöllei
Allison Stevens David Salat
Bruce Fischl
& Jean Augustinack
Oxford/FMRIB
Saad Jbabdi
Tim Behrens
36/41
ONGOING: Registration of
tractography
• Goal: fiber bundle alignment
• Study: compare CVS to methods directly
aligning DWI-derived scalar volumes
• Conclusion: high accuracy cross-subject
registration based on structural MRI
images can provide improved alignment
• Zöllei, Stevens, Huber, Kakunoori, Fischl: “Improved
Tractography Alignment Using Combined Volumetric and
Surface Registration”, accepted to NeuroImage
37
Mean Hausdorff distance measures
for three fiber bundles
CST
ILF
UNCINATE
38
Average tracts after registration mapped to
the template displayed with iso-surfaces
FLIRT
FA-FNIRT
CVS
39
Stages:
•
1. Convert dicom to nifti (creates dwi.nii)
•
2. Eddy current and motion correction using FSLs eddy_correct,
•
creates dwi-ec.nii. Can take 1-2 hours.
•
3. DTI GLM Fit and tensor construction. Includes creation of:
•
tensor.nii -- maps of the tensor (9 frames)
•
eigvals.nii -- maps of the eigenvalues
•
eigvec?.nii -- maps of the eigenvectors
•
adc.nii -- apparent diffusion coefficient
•
fa.nii -- fractional anisotropy
•
ra.nii -- relative anisotropy
•
vr.nii -- volume ratio
•
ivc.nii -- intervoxel correlation
•
lowb.nii -- Low B
•
bvals.dat -- bvalues
•
bvecs.dat -- directions
•
Also creates glm-related images:
•
beta.nii - regression coefficients
•
eres.nii - residual error (log of dwi intensity)
•
rvar.nii - residual variance (log)
•
rstd.nii - residual stddev (log)
•
dwires.nii - residual error (dwi intensity)
•
dwirvar.nii - residual variance (dwi intensity)
•
4. Registration of lowb to same-subject anatomical using
•
FSLs flirt (creates mask.nii and register.dat)
•
5. Map FA to talairach space (creates fa-tal.nii)
•
Example usage:
•
dt_recon --i 6-1025.dcm --s M87102113 --o dti
40
After dt_recon
•
•
•
# Check registration
tkregister2 --mov dti/lowb.nii --reg dti/register.dat \
--surf orig --tag
•
•
•
# View FA on the subject's anat:
tkmedit M87102113 orig.mgz -overlay dti/fa.nii \
-overlay-reg dti/register.dat
•
•
# View FA on fsaverage
tkmedit fsaverage orig.mgz -overlay dti/fa-tal.nii
•
•
•
•
•
•
•
•
•
•
# Group/Higher level GLM analysis:
# Concatenate fa from individuals into one file
# Make sure the order agrees with the fsgd below
mri_concat */fa-tal.nii --o group-fa-tal.nii
# Create a mask:
mri_concat */mask-tal.nii --o group-masksum-tal.nii --mean
mri_binarize --i group-masksum-tal.nii --min .999 --o group-mask-tal.nii
# GLM Fit
mri_glmfit --y group-fa-tal.nii --mask group-mask-tal.nii\
--fsgd your.fsgd --C contrast --glm groupanadir
41