We propose a new tool named Autotract to automate fiber tracking in diffusion tensor imaging (DTI). Autotract uses prior knowledge from a source DTI and a set of corresponding fiber bundles to extract new fibers for a target DTI. Autotract starts by aligning both DTIs and uses the source fibers as seed points to initialize a tractography algorithm. We enforce similarity between the propagated source fibers and automatically traced fibers by computing metrics such as fiber length and fiber distance between the bundles. By analyzing these metrics, individual fiber tracts can be pruned. As a result, we show that both bundles have similar characteristics. Additionally, we compare the automatically traced fibers against bundles previously generated and validated in the target DTI by an expert. This work is motivated by medical applications in which known bundles of fiber tracts in the human brain need to be analyzed for multiple datasets.
Keywords: Tractography; atlas; automatic; diffusion tensor imaging; prior; registration.