Quantitative analysis of diffusion tensor images across subjects using probabilistic tractography

White matter tractography can generate a 3D macroscopic view of the white matter connections in the brain by measuring the diffusion of cellular fluid in tissue and visualize the connections with line strips, tubes, etc. In the past, tractography techniques based on deterministic methodologies have been extensively used. Deterministic tracking follows the primary eigen direction of diffusion that limits its clinical applicability to areas where anisotropy is not clear. More recently, probabilistic tractography methods have been emerging as new tools for extracting fiber tracts. This paper presents a new probability-based framework for quantitative, inter-subject analysis of diffusion tensor imaging data. Advantages of the probabilistic tractography are in its inherent ability to model noise and uncertainty in its global estimation model. This facilitates more accurate tracking in complex neighborhoods, such as branching and crossing fibers. With a combination of statistical measures, our approach can pinpoint abnormalities through the comparison between a set of normal population and patient data. The experiments demonstrate its excellent performance in identifying imaging symptoms of epilepsy and Tourette Syndrome.