Probabilistic fiber tracking using particle filtering

Fan Zhang, Casey Goodlett, Edwin Hancock, Guido Gerig

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    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.

    Original languageEnglish (US)
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings
    PublisherSpringer Verlag
    Number of pages9
    EditionPART 2
    ISBN (Print)9783540757580
    StatePublished - 2007
    Event10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007 - Brisbane, Australia
    Duration: Oct 29 2007Nov 2 2007

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume4792 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science


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