Probabilistic tractography using particle filtering and clustered directional data

The approach of using deterministic methods to trace white-matter fiber tracts through the brain and map brain connectivity is pervasive in currently followed tractographic methodologies. However, using deterministic procedures to support fiber mapping jeopardizes rigorous fiber tractography and may originate deficient maps of white matter fiber networks. We propose a new probabilistic framework for modeling fiber-orientation uncertainty and improve probabilistic tractography. A probabilistic methodology is proposed for estimating intravoxel principal fiber directions, based on clustering directional data arising from orientation distribution function profiles. Mixtures of von Mises-Fisher (vMF) distributions are used to support the probabilistic estimation of intravoxel fiber directions. The fitted parameters of the clustered vMF mixture at each voxel are then used to estimate white-matter pathways using particle filtering techniques. The proposed method is validated on synthetic simulations, as well as on real data experiments. The method holds promise to support robust tractographic methodologies, and build realistic models of white matter tracts in the human brain.