National Alliance for Medical Image Computing UNC: Quantitative DTI Analysis Guido Gerig, Isabelle Corouge Students: Casey Goodlett, Clement Vachet, Matthieu Jomier

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing Conventional Analysis: ROI or voxel-based group tests after alignment Patient Control Quantitative DTI Analysis UNC NA-MIC Approach: Quantitative Analysis of Fiber Tracts DTI Tensor Statistics across/along fiber bundles Statistics of tensors Tracking/ clustering selectionFA FA along tract

National Alliance for Medical Image Computing Processing Tools FibTrac: Input DT-MRI, Filtering, Tensor Calc., FA, ADC, Tractography FiberViewer: Clustering, Bundling, Parametrization, Statistics, Visualization

National Alliance for Medical Image Computing Example: Fiber-tract Measurements 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 uncinate fasciculus FA along uncinate cingulum FA along cingulate Major fiber tracts

National Alliance for Medical Image Computing Processing Steps 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)

National Alliance for Medical Image Computing Tractography ctd. Tracing Source to Target

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

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing ITK Polyline Datastructure

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing 3D Curve Clustering Uncinate fasciculus Longitudinal fasciculus Clustering can separate neighboring bundles Not possible with region-based processing 501 streamlines

National Alliance for Medical Image Computing 3D Curve Clustering Whole longitudinal fasciculus: 2312 streamlines 6 clusters seeding

National Alliance for Medical Image Computing Scan1 Scan2… T B0 1  B0 2 … Scan6 DTI Average Validation: 6 repeated DTI T B0 1  B0 6 Extraction Scan 2… …Scan 6 DTI Average Direct Average of the 6 scans Selection of a ROI Registration of ROI

National Alliance for Medical Image Computing Statistics across 6 repeated scans: Curves of MeanFA and MeanADC, with Standard Deviation FA ADC Tract-based Diffusion Properties

National Alliance for Medical Image Computing Tract-based Diffusion Properties Curves of MeanFA/ MeanADC in comparison to the Average DTI FA ADC

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing 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????

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing 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. / 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: ( Medline citation). Medline citation

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing 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)

National Alliance for Medical Image Computing Local shape properties of wm tracts Geometric characterization of fiber bundles Local shape descriptors: curvature and torsion AdultsNeonate Max. curvature positions: Possible candidates for curve matching