title: Probabilistic tractography using Q-ball modeling and particle filtering.
abstract: By assuming that orientation information of brain white matter fibers can be inferred from Diffusion Weighted 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.
authors: LSIIT, UMR 7005 CNRS-Université de Strasbourg.
authors: Julien
authors: J
authors: Pontabry
authors: François
authors: F
authors: Rousseau
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D011336:Probability