Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling

Fan Zhang, Edwin R. Hancock, Casey Goodlett, Guido Gerig

    Research output: Contribution to journalArticle

    Abstract

    Standard particle filtering technique have previously been applied to the problem of fiber tracking by Brun et al. [Brun, A., Bjornemo, M., Kikinis, R., Westin, C.F., 2002. White matter tractography using sequential importance sampling. In: Proceedings of the ISMRM Annual Meeting, p. 1131] and Bjornemo et al. [Bjornemo, M., Brun, A., Kikinis, R., Westin, C.F., 2002. Regularized stochastic white matter tractography using diffusion tensor MRI, In: Proc. MICCAI, pp. 435-442]. However, these previous attempts have not utilised the full power of the technique, and as a result the fiber paths were tracked in a goal directed way. In this paper, we provide an advanced technique by presenting a fast and novel probabilistic method for white matter fiber tracking in diffusion weighted MRI (DWI), which takes advantage of the weighting and resampling mechanism of particle filtering. We formulate fiber tracking using a non-linear state space model which captures both smoothness regularity of the fibers and the uncertainties in the local fiber orientations due to noise and partial volume effects. Global fiber tracking is then posed as a problem of particle filtering. To model the posterior distribution, we classify voxels of the white matter as either prolate or oblate tensors. We then construct the orientation distributions for prolate and oblate tensors separately. Finally, the importance density function for particle filtering is modeled using the von Mises-Fisher distribution on a unit sphere. Fast and efficient sampling is achieved using Ulrich-Wood's simulation algorithm. Given a seed point, the method is able to rapidly locate the globally optimal fiber and also provides a probability map for potential connections. The proposed method is validated and compared to alternative methods both on synthetic data and real-world brain MRI datasets.

    Original languageEnglish (US)
    Pages (from-to)5-18
    Number of pages14
    JournalMedical Image Analysis
    Volume13
    Issue number1
    DOIs
    StatePublished - Feb 2009

    Fingerprint

    Phosmet
    Sampling
    Diffusion Magnetic Resonance Imaging
    Fibers
    Magnetic resonance imaging
    Tensors
    Space Simulation
    Uncertainty
    Noise
    Seeds
    Importance sampling
    Fiber reinforced materials
    White Matter
    Brain
    Probability density function
    Seed
    Wood

    Keywords

    • Diffusion tensor MRI
    • Particle filtering
    • Probabilistic fiber tracking
    • Tractography
    • von Mises-Fisher sampling

    ASJC Scopus subject areas

    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition
    • Radiology Nuclear Medicine and imaging
    • Health Informatics
    • Radiological and Ultrasound Technology

    Cite this

    Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling. / Zhang, Fan; Hancock, Edwin R.; Goodlett, Casey; Gerig, Guido.

    In: Medical Image Analysis, Vol. 13, No. 1, 02.2009, p. 5-18.

    Research output: Contribution to journalArticle

    Zhang, Fan ; Hancock, Edwin R. ; Goodlett, Casey ; Gerig, Guido. / Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling. In: Medical Image Analysis. 2009 ; Vol. 13, No. 1. pp. 5-18.
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