Fiber Tracking and Fiber Tract Segmentation Using Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) has become a popular tool for noninvasively investigating fiber tract structures. Fiber tracking and tract segmentation are two major tasks in DTI studies. However, fiber crossing is a well known issue in DTI because DTI cannot model crossing fiber orientations (FOs). Therefore, fiber tracking and tract segmentation methods that are able to address crossing fibers are needed. In this thesis, three contributions are made to the development of such fiber tracking and tract segmentation algorithms. First, a fiber tracking method guided by volumetric tract segmentation is presented. Tract segmentation contains anatomical information which can reduce the errors caused by crossing fibers and noise. The FO estimation problem is formulated in a Bayesian framework and the resulting objective function is solved with calculus of variations. The proposed method is able to reduce false positive fibers and generate fibers that correspond to known anatomy. It is also applied to a brain connectome study to show its potential scientific application. Second, we present an algorithm for resolving crossing fibers in situations where limited diffusion gradient directions are achievable. In particular, the algorithm is focused on interdigii