Visual exploration of HARDI fibers with probabilistic tracking

High angular resolution diffusion imaging (HARDI) is an effective method for characterizing complex neural fiber paths in the human brain. However, visualizing and analyzing the fibers is often challenging because of the complexity of the fiber orientation distribution function used to describe the crossing, kissing, and fanning fibers. In this paper, we propose a novel visual analytics approach to study brain fiber paths that allows users to explore fiber bundles to reveal the probability of fiber paths using a new visual classification method. First, we use a spherical deconvolution model for diffusion estimation and a Bayesian theorem for fiber tractography. Second, each fiber is subjected to a cluster analysis using pixel-based visual encoding. This result is shown in a pixel-based visual representation where each pixel bar maps opacity, color, and length to the probability, direction, and length of a fiber. Fiber bundles can then be acquired via a two-step classification routine that uses DBSCAN to group fibers based on similarities. Then the user can further refine fiber bundle selection using probabilistic information from the pixel bars. Therefore, the proposed approach shows not only the shape but also the confidence of the fiber paths. We demonstrate the resulting HARDI fiber bundles and compare a brain with tumor and normal brain using our system. Experiments and an empirical user study verify the effectiveness of our approach.