1. /mhj 1212 Introduction Diffusion Tensor Imaging (DTI) is a fairly new Magnetic Resonance Imaging technique. It shows the diffusion (i.e. random motion) of water molecules in tissue. Diffusion is larger in directions along structures in tissues (e.g. white matter in brain or muscles) than in directions perpendicular to it. The diffusion is anisotropic. The diffusion is characterized by a diffusion tensor, which is a symmetric 3x3 matrix [1]. A tool is developed to visualize this DTI data. Methods Three ways are chosen to visualize DTI data: Color coding From the eigenvalues of the diffusion tensor several anisotropy indices, e.g. the fractional anisotropy (FA), can be derived. These indices provide information about the amount of anisotropy. Figure 1a shows a slice in the brain color coded according the FA. High values indicate high anisotropy. Glyphs Glyphs are icons that represent the local tensor information. Ellipsoids and cuboids can be chosen for this. The ellipsoids and cuboids are scaled according the eigenvectors and eigenvalues. Figure 1b shows an example of cuboids with an MR image on the background as a reference. a)b) Figure 1: a) FA color coded in a brain slice. b) Region indicated in a) with a black square zoomed and glyphs are displayed. Fiber tracking Fiber tracking shows global 3D information about the tensorfield. Fiber tracking mainly simplifies the tensorfield to a vectorfield defined by the main eigenvector of the diffusion tensor. A trajectory is a path tangent to the vectorfield. These trajectories help to give insight about the global structure of the vectorfield. Trajectories are interpreted as nerve fibers in the brain (see figure 2) or muscle fibers (see figure 3a). [2] In positions where fibers are crossing or branching the anisotropy is planar. The first and second eigenvalue are about the same size. This causes that the main eigenvector is not reliable and the vectorfield is not well defined in these areas. A solution to this problem is to track in the directions of the local plane and display a surface in these planar anisotropic areas. If the anisotropy is linear again common fiber tracking is done. An example of a branching fiber path, found with the described Surface Building technique is seen in figure 3b. a)b) Figure 2: Examples of fiber tracking in brain data set. a) Fibers of the corpus callosum. b) Fibers of the Corona Radiata. a)b) Figure 3: a) Fibers in the tiabialis anterior (muscle between ankle and knee) of a mouse. b) Example of a branching fiber path found with the surface building technique. Conclusions The created visualization tool for DTI data shows promising result. It shows the main nerve fiber bundles in brain tissue and shows the orientation of muscle fibers. The Maxima Medical center uses the DTI tool for visualization of data of the neonatal brain. The Magnetic Resonance Laboratory uses it for visualization of mouse muscle. Commisioned by: Supervisor: B.M. ter Haar Romeny Daily supervisor: A. Vilanova Additional committee members: P.A.J. Hilbers, C. van Pul, K. Nicolaij References: [1] D. Le Bihan, e.a. Diffusion Tensor Imaging: Concepts and applications. Journal of Magnetic Resonance Imaging, 13:534-546, 2001 [2] S. Mori, e.a. Fiber tracking: principles and strategies – a technical review. NMR in Biomedicine, (468-480), 2002 Visualization Techniques for Diffusion Tensor Imaging Guus Berenschot BioMedical Imaging and Informatics