Regularized probabilistic tracking with Q-ball fields

Most of the approaches dedicated to fiber tracking use tensor model. This poor approximation of the diffusion process leads to spurious tracking at crossing bundle location. Here we describe an alternative strategy using Qball Model [1] and Monte Carlo sampling. Algorithm description: Water molecule displacement probability in each voxel is given by Qball imaging. From this data we want to track fiber bundles. To resolve this inverse problem, we consider n particles in each starting voxel. Speed of particles is a trade-off between two terms: inertia (vin) and Qball data influence (vout). This influence is randomly chosen from Qball orientation distribution with Gibbs sampler. So v = α vout + (1- α) vin, where α, ranging between 0 and 1, depends on the voxel. This parameter, which is the normalized standard deviation of the Qball distribution, represents local anisotropy. In isotropic voxels the algorithm follows the incident direction, otherwise the Qball distribution is trusted. This memory based approach regularizes the random walk. The particle trajectory is sampled step by step from speed estimation. To regularize further the trajectory curvature, we introduce a cone angle, that depends on incident orientation, to limit the set of directions used by the Gibbs sampler. Materials and methods: Our algorithm is designed to untangle bundle crossing. A diffusion phantom (Fig.1) made up of two haemodialysis fibe