Probabilistic fiber tracking using particle filtering

Authors:
Fan Zhang;Casey Goodlett;Edwin Hancock;Guido Gerig
Affiliations:
Dept. of Computer Science, University of York, York, UK;Dept. of Computer Science, University of North Carolina at Chapel Hill;Dept. of Computer Science, University of York, York, UK;Dept. of Computer Science, University of North Carolina at Chapel Hill
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

This paper presents a novel and fast probabilistic method for white matter fiber tracking from diffusion weighted MRI (DWI). We formulate fiber tracking on a nonlinear state space model which is able to capture both smoothness regularity of fibers and uncertainties of the local fiber orientations due to noise and partial volume effects. The global tracking model is implemented using particle filtering, which allows us to recursively compute the posterior distribution of the potential fibers. The fiber orientation distribution is theoretically formulated for prolate and oblate tensors separately. Fast and efficient sampling is realised using the von Mises-Fisher distribution on unit spheres. Given a seed point, the method is able to rapidly locate the global optimal fiber and also provide a connectivity map. The proposed method is demonstrated on a brain dataset.