Probabilistic fiber tracking using particle filtering and von mises-fisher sampling

Fan Zhang, Casey Goodlett, Edwin Hancock, Guido Gerig

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

    Abstract

    This paper presents a novel and fast probabilistic method for white matter fiber tracking from diffusion weighted magnetic resonance imaging (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. This sequential Monte Carlo technique allows us to recursively compute the posterior distribution of the potential fibers, while there is no limitation on the forms of the prior and observed information. Fast and efficient sampling is realised using the von Mises-Fisher distribution on unit spheres. The fiber orientation distribution is theoretically formulated by combining the axially symmetric tensor model and the formal noise model for DWI. 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 both on synthetic and real-world brain MRI dataset.

    Original languageEnglish (US)
    Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings
    Pages303-317
    Number of pages15
    Volume4679 LNCS
    StatePublished - 2007
    Event6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007 - Ezhou, China
    Duration: Aug 27 2007Aug 29 2007

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4679 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007
    CountryChina
    CityEzhou
    Period8/27/078/29/07

    Fingerprint

    Particle Filtering
    Fiber
    Sampling
    Noise
    Fibers
    Fiber Orientation
    Space Simulation
    Fiber reinforced materials
    Diffusion Magnetic Resonance Imaging
    Von Mises-Fisher Distribution
    Uncertainty
    Seeds
    Observed Information
    Sequential Monte Carlo
    Monte Carlo Techniques
    Probabilistic Methods
    Magnetic Resonance Imaging
    State-space Model
    Magnetic resonance
    Prior Information

    ASJC Scopus subject areas

    • Computer Science(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Theoretical Computer Science

    Cite this

    Zhang, F., Goodlett, C., Hancock, E., & Gerig, G. (2007). Probabilistic fiber tracking using particle filtering and von mises-fisher sampling. In Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings (Vol. 4679 LNCS, pp. 303-317). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4679 LNCS).

    Probabilistic fiber tracking using particle filtering and von mises-fisher sampling. / Zhang, Fan; Goodlett, Casey; Hancock, Edwin; Gerig, Guido.

    Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. Vol. 4679 LNCS 2007. p. 303-317 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4679 LNCS).

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

    Zhang, F, Goodlett, C, Hancock, E & Gerig, G 2007, Probabilistic fiber tracking using particle filtering and von mises-fisher sampling. in Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. vol. 4679 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4679 LNCS, pp. 303-317, 6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007, Ezhou, China, 8/27/07.
    Zhang F, Goodlett C, Hancock E, Gerig G. Probabilistic fiber tracking using particle filtering and von mises-fisher sampling. In Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. Vol. 4679 LNCS. 2007. p. 303-317. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    Zhang, Fan ; Goodlett, Casey ; Hancock, Edwin ; Gerig, Guido. / Probabilistic fiber tracking using particle filtering and von mises-fisher sampling. Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. Vol. 4679 LNCS 2007. pp. 303-317 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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    abstract = "This paper presents a novel and fast probabilistic method for white matter fiber tracking from diffusion weighted magnetic resonance imaging (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. This sequential Monte Carlo technique allows us to recursively compute the posterior distribution of the potential fibers, while there is no limitation on the forms of the prior and observed information. Fast and efficient sampling is realised using the von Mises-Fisher distribution on unit spheres. The fiber orientation distribution is theoretically formulated by combining the axially symmetric tensor model and the formal noise model for DWI. 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 both on synthetic and real-world brain MRI dataset.",
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