Probabilistic tractography using Q-ball modeling and particle filtering

Authors:
Julien Pontabry;François Rousseau
Affiliations:
LSIIT, UMR 7005 CNRS-Université de Strasbourg;LSIIT, UMR 7005 CNRS-Université de Strasbourg
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2011

Citing 2
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Abstract

By assuming that orientation information of brain white matter fibers can be inferred from DiffusionWeighted Magnetic Resonance Imaging (DWMRI) measurements, tractography algorithms provide an estimation of the brain connectivity in-vivo. The two key ingredients of tractography are the diffusion model (tensor, high-order tensor, Q-ball, etc.) and the way to deal with uncertainty during the tracking process (deterministic vs probabilistic). In this paper, we investigate the use of an analytical Q-ball model for the diffusion data within a well-formalized particle filtering framework. The proposed method is validated and compared to other tracking algorithms on the MICCAI'09 contest Fiber Cup phantom and on in-vivo brain DWMRI data.