Stochastic Two-Tensor Fibre Tractography

Diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive in-vivo imaging technique that can be used to generate fibre trajectories in brain white matter. Many current tractography methods assume that the fibre direction coincides with the principal eigenvector of a single diffusion tensor. This is, however, not the case for regions with crossing fibres. In addition noise introduces more uncertainty and makes the computation of the fibre direction difficult. Multi-tensor fibre tracking can alleviate the problems when crossing fibres are encountered. Stochastic fibre tracking techniques overcome the uncertainties of deterministic methods by adding a degree of randomness to deterministic tractography. We propose an algorithm using a stochastic fibre tracking approach based on two tensors. The method is verified on a synthetic dataset and an in-vivo