A statistical shape model of individual fiber tracts extracted from diffusion tensor MRI

Isabelle Corouge, Sylvain Gouttard, Guido Gerig

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

    Abstract

    Diffusion Tensor MRI has become the preferred imaging modality to explore white matter structure and brain connectivity in vivo. Conventional region of interest analysis and voxel-based comparison does not make use of the geometric properties of fiber tracts. This paper explores shape modelling of major fiber bundles. We describe tracts, represented as clustered sets of curves of similar shape, by a shape prototype swept along a space trajectory. This approach can naturally describe white matter structures observed either as bundles dispersing towards the cortex or tracts defined as dense patterns of parallel fibers. Sets of streamline curves obtained from tractography are clustered, parametrized and aligned with a similarity transform. An average curve and eigenmodes of shape variation describe a compact statistical shape model. Reconstruction by sweeping the template along the trajectory results in a simplified model of a tract. Feasibility is demonstrated by modelling callosal and cortico-spinal fasciculi of two different subjects.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science
    EditorsC. Barillot, D.R. Haynor, P. Hellier
    Pages671-679
    Number of pages9
    Volume3217
    Edition1 PART 2
    StatePublished - 2004
    EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France
    Duration: Sep 26 2004Sep 29 2004

    Other

    OtherMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings
    CountryFrance
    CitySaint-Malo
    Period9/26/049/29/04

    Keywords

    • Diffusion Tensor Imaging
    • Statistical shape modelling

    ASJC Scopus subject areas

    • Computer Science (miscellaneous)

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