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UNC: Quantitative DTI Analysis

UNC: Quantitative DTI Analysis. Guido Gerig, Isabelle Corouge Students: Casey Goodlett, Clement Vachet, Matthieu Jomier. UNC: Quantitative DTI Analysis. Clinical needs: Access to fiber tract properties: WM “Integrity”

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UNC: Quantitative DTI Analysis

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  1. UNC: Quantitative DTI Analysis Guido Gerig, Isabelle Corouge Students: Casey Goodlett, Clement Vachet, Matthieu Jomier

  2. UNC: Quantitative DTI Analysis • Clinical needs: • Access to fiber tract properties: WM “Integrity” • Fibertract-oriented measurements: Diffusion properties within cross-sections and along bundles • Statistics of diffusion tensors: Beyond FA/ADC • Approaches: • Replace voxel-based by fiber-tract-based analysis • FiberViewer: Set of tools for quantitative fiber tract analysis: Geometry and Diffusion Properties • Clustering, Outlier Detection, Parametrization, Establishing inter-subject correspondence • Statistical analysis of DTI

  3. UNC NA-MIC Approach: • Quantitative Analysis of Fiber Tracts • DTI Tensor Statistics across/along fiber bundles • Statistics of tensors Conventional Analysis: ROI or voxel-based group tests after alignment Patient Control selection FA Tracking/ clustering FA along tract Quantitative DTI Analysis

  4. Processing Tools FiberViewer: Clustering, Bundling, Parametrization, Statistics, Visualization FibTrac: Input DT-MRI, Filtering, Tensor Calc., FA, ADC, Tractography

  5. Example: Fiber-tract Measurements cingulum Major fiber tracts FA along cingulate uncinate fasciculus uncinate fasciculus FA along uncinate Corouge, Isabelle, Gouttard, Sylvain and Gerig, Guido, "Towards a Shape Model of White Matter Fiber Bundles using Diffusion Tensor MRI" , Proc. IEEE Computer Society, Int. Symp. on Biomedical Imaging, to appear April 2004 Gerig, Guido, Gouttard, Sylvain and Corouge, Isabelle, "Analysis of Brain White Matter via Fiber Tract Modeling", full paper IEEE Engineering in Medicine and Biology Society EMBS 2004, Sept. 2004

  6. Tractography Data structure for sets of attributed streamlines Clustering Parametrization Diffusion properties across/along bundles Graph/Text Output Statistical Analysis Slicer (?) ITK Polyline data structure (J. Jomier) Normalized Cuts (ITK) B-splines (ITK) NEW: DTI stats in nonlinear space (UTAH) Display/Files Biostatistics / ev. DTI hypothesis testing (UTAH) Processing Steps

  7. Concept: Statistics along fiber tracts Origin (anatomical landmark) FA

  8. Accomplished 09/04 – 02/05 FiberViewer Prototype System (ITK) • Clustering (various metrics, normalized graph cut) • Parametrization • FA/ADC/Eigen-value Statistics • Uses SpatialObjects and SpatialObject-Viewer • ITK Datastructure for attributed streamlines • Tests in two UNC clinical studies (neonates, autism) • Validation of reproducibility: ISMRM’05

  9. ITK Polyline Datastructure

  10. 3D Curve Clustering with Normalized Graph Cuts • NGC: Shi and Malik, IEEE 2000 • Set-up of Matrix: Metric: Mean of distances at corresponding points of parametrized curves • Matlab prototype ready, ITK version in development (Casey Goodlett, UNC) Graph Cut

  11. 3D Curve Clustering Longitudinal fasciculus 501 streamlines Uncinate fasciculus Clustering can separate neighboring bundles Not possible with region-based processing

  12. 3D Curve Clustering seeding Whole longitudinal fasciculus: 2312 streamlines 6 clusters

  13. Validation: 6 repeated DTI Registration of ROI Extraction T B01B02 Selection of a ROI Scan 2… Scan2… T B01B06 Scan1 Extraction … Scan6 …Scan 6 Extraction Direct Average of the 6 scans DTI Average DTI Average

  14. Tract-based Diffusion Properties Statistics across 6 repeated scans: Curves of MeanFA and MeanADC, with Standard Deviation FA ADC FA

  15. Tract-based Diffusion Properties Curves of MeanFA/ MeanADC in comparison to the Average DTI FA ADC FA

  16. Work in Progress: Statistics of Tensors (UTAH & UNC) • Statistics of DTI requires new math and tools • Linear Statistics does not preserve positive-definit. • Tom Fletcher UNC PhD 2004 (w. Joshi/Pizer), now UTAH • Riemannian symmetric (nonlinear) space • New similarity measure • Method for interpolation of tensors

  17. we all like to pick the highlights, who picks the “dirty reality” problems?? • Papers: “Bad slices were eliminated from processing” • But: +12 dir/ +4 averages / +25 slices:1200 images????

  18. we all like to pick the highlights, who picks the “dirty reality” problems?? • UNC Solution: ITK DTIchecker (Matthieu Jomier) • Automatic screening for intensity artifacts, motion artifacs, missing/corrupted slices • Writes report / Script file

  19. we all like to pick the highlights, who picks the “dirty reality” problems?? • Lucas MRI and MRS Center, Stanford University, CA : Spin echo EPI dti_epi Pulsed Gradient/Stejskal-Tanner diffusion weighting • UNC uses Stanford Bammer/Mosley “tensorcalc” software for DTI processing • Eddy Current Distortion Correction (here 23 directions) • Tensorcalc (“T1”) DWI/DTI recon toolbox with powerful built-in image registration tools. http://rsl.stanford.edu/research/software.html / http://www-radiology.stanford.edu/majh/ • http://snarp.stanford.edu/dwi/maj/ The diffusion weighted images are unwarped using the method described in de Crespigny, A.J. and Moseley, M.E.: "Eddy Current Induced Image Warping in Diffusion Weighted EPI", Proc , ISMRM 6th Meeting, Sydney 661 (1998) and Haselgrove, J.C. and Moore, J.R., "Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient", MRM 1996, 36:960-964 ( Medline citation).

  20. Next 6 months • Methodology Development: • DTI tensor statistics: close collab. with UTAH • Deliver ITK tools for clustering/parametrization to Core 2 • Feasibility tests with tractography from Slicer • Deliver FiberViewer prototype platform to Core 2 to discuss integration into Slicer • Clinical Study: DTI data from Core 3 • Check feasibility of tract-based analysis w.r.t. DTI resolution (isotropic voxels(?)), SNR • Apply procedure to measure properties of: • Cingulate (replicate ROI findings of Shenton/Kubiki) • Uncinate fasciculus (replicate ROI findings) • Dartmouth 3mm DTI data

  21. NA-MIC DTI Processing Needs • Generic DTI reconstruction • Arbitrary #directions • Artifact checking/removal • Eddy-current distortion correction • Tensor calculation • Tensor Filtering (nonlinear, geodesic space) • Tensor interpolation, linear- and nonlinear registration • Tensor+ reconstruction/representation (DSI) • Standards for datastructures (DTI, tensors, streamlines, diffusion-gradient-file)

  22. Local shape properties of wm tracts • Geometric characterization of fiber bundles • Local shape descriptors: curvature and torsion Adults Neonate Max. curvature positions: Possible candidates for curve matching

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