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Introduction to Diffusion MRI processing. The diffusion process. http://pubs.niaaa.nih.gov/publications/arh27-2/146-152.htm. dt_recon. Required Arguments: --i invol --s subjectid --o outputdir Example: dt_recon --i dt_recon --i 6-1025.dcm --s M111 --o dti. Main processing steps.
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The diffusion process http://pubs.niaaa.nih.gov/publications/arh27-2/146-152.htm
dt_recon • Required Arguments: • --i invol • --s subjectid • --o outputdir • Example: dt_recon --i dt_recon --i 6-1025.dcm --s M111 --o dti
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) • # Mappingmask, FA to Talairach space
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
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 • …
Tractography examples • Trackvis and Diffusion Toolkit (http://www.trackvis.org/)
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
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 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
? 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, …]
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
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
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
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
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 …
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
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
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
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
SLF CST Reliability study Manual labeling by Allison Stevens and Cibu Thomas Visualization tool by Ruopeng Wang
Test-retest reliability No info from training subjects With info from training subjects Visit 1 Visit 1 Visit 2 Visit 2
Application: Huntington’s disease Data courtesy of Dr. D. Rosas, MGH Healthy Huntington’s stage 1 Huntington’s stage 3 Huntington’s stage 2
MD changes in patients SLF1 SLF2 CST SLF3 0.1 Cingulum 0.001 P-values for T-test on mean MD of Huntington’s patients (N=33) and controls (N=22)
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 (N=25) and controls (N=18)
* * * * * ° ° * FA and RD changes * p<.05 ° p<.10 Left cingulum Right cingulum
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!
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
Acknowledgements MGH/Martinos Lilla Zöllei Allison Stevens David Salat Bruce Fischl & Jean Augustinack Oxford/FMRIB Saad Jbabdi Tim Behrens
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
CST ILF UNCINATE Mean Hausdorff distance measures for three fiber bundles
FLIRT FA-FNIRT CVS Average tracts after registration mapped to the template displayed with iso-surfaces
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
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