Introduction to Diffusion MRI processing

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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)

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Examples of scalar maps

• FA fractional anisotropy (fiber density, axonal
  diameter, myelination in WM)
• RA relative anisotropy
• VR volume ratio
• IVC inter-voxel correlation (diffusion
  orientation agreement in neighbors)
• ADC apparent diffusion coefficient (magnitude of
  diffusion low value ? organized tracts)
• RD radial diffusivity
• AD axial diffusivity

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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.trackvi
  s.org/)

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CST on (color) FA map
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Under developmentTRActs Constrained by
UnderLying Anatomy (TRACULA)

• Anastasia Yendiki
• HMS/MGH/MIT Athinoula A. Martinos Center for
  Biomedical Imaging

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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

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Diffusion MRI
Diffusion encoding in direction g1

• 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

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, ODonnell 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

• 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 non-dominant connection? We can
  define waypoints, but theres no guarantee that
  well reach them.

• Not symmetric between tract start and end
  point

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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

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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

• Multiple diffusion compartments in each voxel
• Anisotropic compartments that model fibers (1, 2,
  )
• One isotropic compartment that models everything
  left over (0)

1
0
2

• 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
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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 Huntingtons disease
Data courtesy of Dr. D. Rosas, MGH
Healthy
Huntingtons stage 1
Huntingtons stage 3
Huntingtons stage 2
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MD changes in patients
SLF1
SLF2
CST
SLF3
0.1
Cingulum
0.001
P-values for T-test on mean MD of Huntingtons
patients (N33) and controls (N22)
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Correlation with disease stage
Left Left Left Left Left Right Right Right Right
CST SLF1 SLF2 SLF3 CB SLF1 SLF2 SLF3 CB
FA -.3 -.3 -.3 -.3 -.3 -.3 -.2 -.5 -.2
MD .3 .4 .7 plt10-7 .6 plt10-5 .4 .5 .7 plt10-8 .6 plt10-5 .3
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
SLF1
SLF2
CST
SLF3
0.1
Cingulum
0.001
P-values for T-test on mean RD of schizophrenia
patients (N25) and controls (N18)
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FA and RD changes
plt.05 plt.10
Left cingulum
Right cingulum
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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
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Average tracts after registration mapped to the
template displayed with iso-surfaces
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• 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)

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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.ni
  i --mean
• mri_binarize --i group-masksum-tal.nii --min .999
  --o group-mask-tal.nii
• GLM Fit