Improved correspondence for DTI population studies via unbiased atlas building

Casey Goodlett, Brad Davis, Remi Jean, John Gilmore, Guido Gerig

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

    Abstract

    We present a method for automatically finding correspondence in Diffusion Tensor Imaging (DTI) from deformable registration to a common atlas. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The registration image match metric uses a feature detector for thin fiber structures of white matter, and interpolation and averaging of diffusion tensors use the Riemannian symmetric space framework. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.

    Original languageEnglish (US)
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings
    PublisherSpringer Verlag
    Pages260-267
    Number of pages8
    ISBN (Print)354044727X, 9783540447276
    StatePublished - Jan 1 2006
    Event9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - Copenhagen, Denmark
    Duration: Oct 1 2006Oct 6 2006

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4191 LNCS - II
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
    CountryDenmark
    CityCopenhagen
    Period10/1/0610/6/06

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    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Goodlett, C., Davis, B., Jean, R., Gilmore, J., & Gerig, G. (2006). Improved correspondence for DTI population studies via unbiased atlas building. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings (pp. 260-267). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4191 LNCS - II). Springer Verlag.