Probabilistic fiber tracking using particle filtering

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 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
    Pages144-152
    Number of pages9
    Volume4792 LNCS
    EditionPART 2
    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)03029743
    ISSN (Electronic)16113349

    Other

    Other10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007
    CountryAustralia
    CityBrisbane
    Period10/29/0711/2/07

    Fingerprint

    Particle Filtering
    Fiber
    Fibers
    Fiber Orientation
    Phosmet
    Space Simulation
    Fiber reinforced materials
    Diffusion Magnetic Resonance Imaging
    Von Mises-Fisher Distribution
    Uncertainty
    Noise
    Seeds
    Probabilistic Methods
    State-space Model
    Unit Sphere
    Posterior distribution
    Magnetic resonance imaging
    Brain
    Tensors
    Nonlinear Model

    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. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings (PART 2 ed., Vol. 4792 LNCS, pp. 144-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4792 LNCS, No. PART 2).

    Probabilistic fiber tracking using particle filtering. / Zhang, Fan; Goodlett, Casey; Hancock, Edwin; Gerig, Guido.

    Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings. Vol. 4792 LNCS PART 2. ed. 2007. p. 144-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4792 LNCS, No. PART 2).

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

    Zhang, F, Goodlett, C, Hancock, E & Gerig, G 2007, Probabilistic fiber tracking using particle filtering. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings. PART 2 edn, vol. 4792 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4792 LNCS, pp. 144-152, 10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007, Brisbane, Australia, 10/29/07.
    Zhang F, Goodlett C, Hancock E, Gerig G. Probabilistic fiber tracking using particle filtering. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings. PART 2 ed. Vol. 4792 LNCS. 2007. p. 144-152. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
    Zhang, Fan ; Goodlett, Casey ; Hancock, Edwin ; Gerig, Guido. / Probabilistic fiber tracking using particle filtering. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings. Vol. 4792 LNCS PART 2. ed. 2007. pp. 144-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
    @inproceedings{f1625bf4cb854ee3ba8585f4c1e50acd,
    title = "Probabilistic fiber tracking using particle filtering",
    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.",
    author = "Fan Zhang and Casey Goodlett and Edwin Hancock and Guido Gerig",
    year = "2007",
    language = "English (US)",
    isbn = "9783540757580",
    volume = "4792 LNCS",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    number = "PART 2",
    pages = "144--152",
    booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings",
    edition = "PART 2",

    }

    TY - GEN

    T1 - Probabilistic fiber tracking using particle filtering

    AU - Zhang, Fan

    AU - Goodlett, Casey

    AU - Hancock, Edwin

    AU - Gerig, Guido

    PY - 2007

    Y1 - 2007

    N2 - 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.

    AB - 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.

    UR - http://www.scopus.com/inward/record.url?scp=38349071910&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=38349071910&partnerID=8YFLogxK

    M3 - Conference contribution

    SN - 9783540757580

    VL - 4792 LNCS

    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    SP - 144

    EP - 152

    BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings

    ER -