Transcript LZ.fs.dt_recon-intro - Athinoula A. Martinos Center for

Introduction to Diffusion MRI
processing
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The diffusion process
http://pubs.niaaa.nih.gov/publications/arh27-2/146-152.htm
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dt_recon
• Required Arguments:
• --i invol
• --s subjectid
• --o outputdir
• Example: dt_recon --i
dt_recon --i 6-1025.dcm --s M111 --o dti
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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
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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
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Examples of scalar maps


• FA: fractional anisotropy (fiber density, axonal
3                

2 
  
diameter, myelination in WM)
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
• 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
• …
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FA
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ADC
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IVC
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Tractography examples
• Trackvis and Diffusion Toolkit
(http://www.trackvis.org/)
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CST on (color) FA map
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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
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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
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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, …]
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Local vs. global
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• Local: Uses local information to determine next step,
errors propagate from areas of high uncertainty
• Global: Integrates information along the entire path
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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
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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
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Global tractography
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• 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
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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
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Multi-compartment model
Behrens et al., MRM ‘03
Jbabdi et al., NeuroImage ‘07
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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
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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
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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
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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
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Reliability study
Manual labeling by Allison Stevens and Cibu Thomas
Visualization tool by Ruopeng Wang
CST
SLF
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Test-retest reliability
No info from training subjects
With info from training subjects
Visit 1
Visit 1
Visit 2
Visit 2
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Application: Huntington’s disease
Data courtesy of Dr. D. Rosas, MGH
Healthy
Huntington’s stage 1
Huntington’s stage 2
Huntington’s stage 3
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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)
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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
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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)
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FA and RD changes
*
*
*
°
*
*
*
°
* p<.05
° p<.10
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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!
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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
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Acknowledgements
MGH/Martinos
Lilla Zöllei
Allison Stevens David Salat
Bruce Fischl
& Jean Augustinack
Oxford/FMRIB
Saad Jbabdi
Tim Behrens
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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
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Mean Hausdorff distance measures
for three fiber bundles
CST
ILF
UNCINATE
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Average tracts after registration mapped to
the template displayed with iso-surfaces
FLIRT
FA-FNIRT
CVS
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Stages:
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1. Convert dicom to nifti (creates dwi.nii)
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2. Eddy current and motion correction using FSLs eddy_correct,
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creates dwi-ec.nii. Can take 1-2 hours.
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3. DTI GLM Fit and tensor construction. Includes creation of:
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tensor.nii -- maps of the tensor (9 frames)
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eigvals.nii -- maps of the eigenvalues
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eigvec?.nii -- maps of the eigenvectors
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adc.nii -- apparent diffusion coefficient
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fa.nii -- fractional anisotropy
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ra.nii -- relative anisotropy
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vr.nii -- volume ratio
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ivc.nii -- intervoxel correlation
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lowb.nii -- Low B
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bvals.dat -- bvalues
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bvecs.dat -- directions
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Also creates glm-related images:
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beta.nii - regression coefficients
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eres.nii - residual error (log of dwi intensity)
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rvar.nii - residual variance (log)
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rstd.nii - residual stddev (log)
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dwires.nii - residual error (dwi intensity)
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dwirvar.nii - residual variance (dwi intensity)
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4. Registration of lowb to same-subject anatomical using
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FSLs flirt (creates mask.nii and register.dat)
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5. Map FA to talairach space (creates fa-tal.nii)
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Example usage:
•
dt_recon --i 6-1025.dcm --s M87102113 --o dti
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After dt_recon
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# Check registration
tkregister2 --mov dti/lowb.nii --reg dti/register.dat \
--surf orig --tag
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# View FA on the subject's anat:
tkmedit M87102113 orig.mgz -overlay dti/fa.nii \
-overlay-reg dti/register.dat
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# View FA on fsaverage
tkmedit fsaverage orig.mgz -overlay dti/fa-tal.nii
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# 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
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