1. Diffusion Tensor Imaging (DTI) is becoming a routine technique to study white matter properties and alterations of fiber integrity due to pathology. The advanced MRI technique needs post processing by adequate image analysis and visualization tools. We have developed an integrated software package for efficient processing, fiber* tracking, and interactive visualization of DTI data. This allows even non-expert to explore DTI data and to obtain results that so far were exclusive to research teams with strong computer science support. The tool guides a user through the various processing stages including tensor calculation, calculation of fractional anisotropy (FA) and apparent diffusion coefficient (ADC), extraction of fiber* bundles between source and target regions of interest (ROI), and 2-D and 3-D visualization of diffusion images and fibers*. * The term “fiber” is used to describe streamlines extracted from tensor fields. These streamlines represent diffusion properties of wm bundles but not individual fibers. Motivation: Analysis Tool For Diffusion Tensor MRI Pierre Fillard 1 and Guido Gerig 1,2 1 Depts. of CS, 2 Psychiatry, University of North Carolina at Chapel Hill 1 Depts. of CS, 2 Psychiatry, University of North Carolina at Chapel Hill Program features: Result of the reconstruction of 4 major fibers tracts (blue: cortico-spinal tract, red: splenium and genu tracts, yellow: longitudinal fasciculi, green: cingulate) The DTI processing tool is freely available at http://midag.cs.unc.edu This software has been developed in ITK (NLM sponsored Insight Toolkit), a powerful C++ library dedicated to medical image processing. Currently, the DTI tool is available for Windows ® PC, Linux and UNIX Sun Solaris. What’s behind: STEP 1: Loading of the DTI dataset into the tool The user can switch between the diffusion tensor images through a simple list. In the example, the B0 image is displayed as orthogonal cuts and a 3D view by three orthogonal slices. STEP 2: FA and ADC maps calculation with a simple click The Processing tab allows to choose between FA (Fractional Anisotropy) and ADC (Apparent Diffusion Coefficient) maps calculation. These maps can be stored as 3D image data. STEP 3: Manual ROI definition with IRIS/SNAP The FA map has been loaded into IRIS/SNAP and a ROI has been manually placed, here at the top of the corpus callosum. This ROI is then stored as a 3D image data set. Fiber Reconstruction Step by Step: Full screen view Output formats Binary image: a voxel is set to 1 if a fiber passes through, 0 otherwise. Fiber file format: list of spatial coordinates of the center line of the reconstructed fibers (ITK data format for curvilinear structures). Example of a fiber-label image overlapped on the FA map. The surface defined by the fiber bundle has been rendered in 3D. Cumulative mode: bright areas of the image correspond to high density fiber regions. The DTI Processing tool GUI showing 2-D and 3-D visualization options (ROIs, streamlines and FA level surface). The ROI is finally loaded into the tool and the resulting tracking is shown in a 3D window. The fibers are represented by 3D poly-tubes (ITK format). Top right: 3-D vector field view (axial plane). STEP 4: Loadingof theROI and fiber reconstruction STEP 4: Loading of the ROI and fiber reconstruction Cumulative image: voxel values are directly related to the “density” of fibers. Loading input DTI data (7 volumetric images, GIPL, Analyze or DICOM- META format). Estimation of the Tensor field and calculation of ADC and FA maps. 2-D orthogonal slice visualization of DTI data and of ADC and FA images. 3-D vector field visualization. Loading label image with user-defined Regions Of Interest (ROIs). Fiber tracking: a) from target to source ROIs b) from full brain to source ROI. 3-D interactive visualization of fiber bundles, FA isosurface, source and target ROIs, and of user-selected image channels. Storing of fiber bundles as sets of poly-lines or as binary fiber-tract label images. Storing of FA, ADC image data and of 3-D displays. MICCAI 2003, Nov. 2003