1 Introduction to Diffusion MRI processing

2 The diffusion process

3 dt_recon Required Arguments: --i invol --s subjectid --o outputdir Example: dt_recon --i dt_recon --i dcm --s M111 --o dti

4 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

5 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

6 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 …

7 FA

8 ADC

9 IVC

10 Tractography examples Trackvis and Diffusion Toolkit (

11

12 CST on (color) FA map

Under development: TRActs Constrained by UnderLying Anatomy (TRACULA) Anastasia Yendiki HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging

14/41 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

Diffusion in brain tissue Gray matter: Diffusion is unrestricted  isotropic White matter: Diffusion is restricted  anisotropic Differentiate tissues based on the diffusion (random motion) of water molecules within them

16/41 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 No diffusion encoding Diffusion encoding in direction g 1 g2g2 g3g3 g4g4 g5g5 g6g6

17/41 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, …]

18/41 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     

19/41 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

20/41 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 non- dominant connection? We can define waypoints, but there’s no guarantee that we’ll reach them. Not symmetric between tract “start” and “end” point

21/41 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     

22/41 Probabilistic tractography 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 Have set of images … Want most probable path

23/41 Multi-compartment model Multiple diffusion compartments in each voxel: –Anisotropic compartments that model fibers (1, 2, …) –One isotropic compartment that models everything left over (0) Behrens et al., MRM ‘03 Jbabdi et al., NeuroImage ‘07 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 1 2 0

24/41 Anatomical priors for WM paths 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 WM pathways are well-constrained by surrounding anatomy

25/41 TRACULA Manual labeling of paths on a set of training subjects, performed by an expert Anatomical segmentation maps of the training subjects, produced by FreeSurfer 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

26/41 Preliminary results 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 –T 1 : 1x1x1 mm resolution Use manual labeling of 9 subjects to obtain path priors and path initialization for 10th subject Data courtesy of Dr. R. Gollub, MGH

27/41 Reliability study Manual labeling by Allison Stevens and Cibu Thomas Visualization tool by Ruopeng Wang CST SLF

28/41 Test-retest reliability No info from training subjectsWith info from training subjects Visit 1 Visit 2

29/41 Application: Huntington’s disease HealthyHuntington’s stage 1 Huntington’s stage 3 Huntington’s stage 2 Data courtesy of Dr. D. Rosas, MGH

30/41 MD changes in patients CST SLF1SLF2 SLF3 Cingulum P-values for T-test on mean MD of Huntington’s patients (N=33) and controls (N=22)

31/41 Correlation with disease stage LeftRight CSTSLF1SLF2SLF3CBSLF1SLF2SLF3CB FA MD p< p< p< p< RD AD CST:Corticospinal tract SLF:Superior longitudinal fasciculus CB:Cingulum body FA:Fractional anisotropy MD:Mean diffusivity RD:Radial diffusivity AD:Axial diffusivity

32/41 Application: Schizophrenia Data courtesy of Dr. R. Gollub, MGH P-values for T-test on mean RD of schizophrenia patients (N=25) and controls (N=18) CST SLF1SLF2 SLF3 Cingulum

33/41 * p<.05 ° p<.10 * * * * ° ° * * FA and RD changes Left cingulum Right cingulum

34/41 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!

35/41 Acknowledgements Support provided in part by: National Center for Research Resources –P41 RR14075 –R01 RR16594 –The NCRR BIRN Morphometric Project BIRN002, U24 RR National Institute for Biomedical Imaging and Bioengineering –K99 EB –R01 EB –R01 EB National Institute for Neurological Disorders and Stroke –R01 NS Mental Illness and Neuroscience Discovery (MIND) Institute National Alliance for Medical Image Computing –Funded by the NIH Roadmap for Medical Research, grant U54 EB005149

36/41 Acknowledgements Lilla Zöllei Allison Stevens David Salat Bruce Fischl Saad Jbabdi Tim Behrens MGH/Martinos Oxford/FMRIB & Jean Augustinack

37 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

38 Mean Hausdorff distance measures for three fiber bundles CSTILFUNCINATE

39 Average tracts after registration mapped to the template displayed with iso-surfaces FLIRTFA-FNIRTCVS

40 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 dcm --s M o dti

41 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 M 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 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