1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org NA-MIC UNC Guido Gerig, Martin Styner, Isabelle Corouge http://na-mic.org

2. National Alliance for Medical Image Computing http://na-mic.org UNC: Quantitative DTI Analysis Guido Gerig, Isabelle Corouge Students: Casey Goodlett and Clement Vachet

3. National Alliance for Medical Image Computing http://na-mic.org 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

4. National Alliance for Medical Image Computing http://na-mic.org 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. 2004 uncinate fasciculus FA along uncinate cingulum FA along cingulate Major fiber tracts

5. National Alliance for Medical Image Computing http://na-mic.org Processing Steps Tractography –Data structure for sets of attributed streamlines Clustering Parametrization Diffusion properties across/along bundles Graph/Text Output Statistical Analysis  Slicer (?)  ITK DTI Fiber Spatial Object 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)

6. National Alliance for Medical Image Computing http://na-mic.org Results FiberViewer Prototype (ITK) Clustering (various metrics) Parametrization FA/ADC/Eigen-value Statistics Uses SpatialObjects and SpatialObject-Viewer Used in two UNC clinical studies (neonates, autism) Validation: ISMRM’05

7. National Alliance for Medical Image Computing http://na-mic.org Next 6 months Methodology Development: –DTI tensor statistics: close collab. with UTAH –Deliver ITK tools for clustering/parameterization to Core 2 –Feasibility tests with tractography from Slicer –Deliver 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) Uncinate fasciculus (replicate ROI findings) Other tracts of interest

8. National Alliance for Medical Image Computing http://na-mic.org UNC: Statistical Shape Analysis Martin Styner Students: Ipek Oguz and Christine Shun Xu

9. National Alliance for Medical Image Computing http://na-mic.org Shape Analysis Pipeline Clinical need: Localization of shape and volume changes 3D objects of spherical topology Input: Segmentation from models or binary images Modeling Steps: –Individual surface models Regularization Correspondence –Alignment via Procrustes & choice of scale –Skeletal description Structural subdivision Statistical analysis of models

10. National Alliance for Medical Image Computing http://na-mic.org Shape Analysis Pipeline Thickness maps –Distance to skeleton Local shape analysis –To template or template-free –Univariate Euclidean distance –Multivariate Hotelling T 2 distance –Raw p values, t/T 2 -maps, effect-size –Conservative correction for Type II error MIT discriminative analysis complements our shape analysis well Visualizations of steps for QC

11. National Alliance for Medical Image Computing http://na-mic.org Next 6 months NAMIC toolkit development –Standardization of IO & internal representation With MIT & Georgia Tech –Standardization of visualization tools –Automation of tools, transfer to standard Methodology development –Non-Euclidean shape metrics with permutation tests –Probabilistic structural subdivision method –3D visualization maps of statistical metrics Clinical: Shape analysis data from Core 3 –Feasibility of shape analysis on data from Core 3 –Caudate shape analysis on Brockton VA/Harvard data