Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data

Axel Elaldi, Neel Dey, Heejong Kim, Guido Gerig

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

    Abstract

    We present a rotation-equivariant self-supervised learning framework for the sparse deconvolution of non-negative scalar fields on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tissue structure such as white matter. Due to spatial and spectral partial voluming, clinically-feasible dMRI struggles to resolve crossing-fiber white matter configurations, leading to extensive development in spherical deconvolution methodology to recover underlying fiber directions. However, these methods are typically linear and struggle with small crossing-angles and partial volume fraction estimation. In this work, we improve on current methodologies by nonlinearly estimating fiber structures via self-supervised spherical convolutional networks with guaranteed equivariance to spherical rotation. We perform validation via extensive single and multi-shell synthetic benchmarks demonstrating competitive performance against common baselines. We further show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset. Finally, we show downstream improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.

    Original languageEnglish (US)
    Title of host publicationInformation Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings
    EditorsAasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages267-278
    Number of pages12
    ISBN (Print)9783030781903
    DOIs
    StatePublished - 2021
    Event27th International Conference on Information Processing in Medical Imaging, IPMI 2021 - Virtual, Online
    Duration: Jun 28 2021Jun 30 2021

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12729 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference27th International Conference on Information Processing in Medical Imaging, IPMI 2021
    CityVirtual, Online
    Period6/28/216/30/21

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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