Diffusion tensor field visualization

Abstract

Visualization of diffusion in restricted media, such as the fiber network in human brain, via MR imaging provides a better understanding of the underlying micro structure and allows us to study it in detail. However, 3D tensor field visualization presents several challenges. In this study, we present an interactive tool for visualizing 3D diffusion tensor fields. The tool is composed of two approaches. The first one, the Tensor Paint, is based on the definition of local connectivity between neighboring tensor pairs. The connectivity value together with a threshold determines whether a tensor passes the paint it has received to its neighbors or not. Tensor painting employs a number of different connectivity definitions. They are all based on the interpretation of the diffusion tensors as the covariance matrices of the 3D Gaussian PDFs representing the spatial distribution of moving particles. The second approach is based on the modulation of an input white noise texture by the tensor field. For this purpose, we have utilized the Line Integral Convolution (LIC) technique which is a widely used technique in flow visualization. In LIC, the application of a convolution filter follows local streamline computation. In the study, we first discuss the requirements for the visualization of diffusion tensor fields and then present our tool. Presentation of the results and evaluations obtained on two phantom diffusion tensor fields and a discussion follows this.