Detection of Crossing White Matter Fibers with High-Order Tensors and Rank-k Decompositions

Fangxiang Jiao, Yaniv Gur, Chris R. Johnson, Sarang Joshi

Research output: Chapter in Book/Report/Conference proceedingChapter

24 Scopus citations

Abstract

Fundamental to high angular resolution diffusion imaging (HARDI), is the estimation of a positive-semidefinite orientation distribution function (ODF) and extracting the diffusion properties (e.g., fiber directions). In this work we show that these two goals can be achieved efficiently by using homogeneous polynomials to represent the ODF in the spherical deconvolution approach, as was proposed in the Cartesian Tensor-ODF (CT-ODF) formulation. Based on this formulation we first suggest an estimation method for positive-semidefinite ODF by solving a linear programming problem that does not require special parameterization of the ODF. We also propose a rank-k tensor decomposition, known as CP decomposition, to extract the fibers information from the estimated ODF. We show that this decomposition is superior to the fiber direction estimation via ODF maxima detection as it enables one to reach the full fiber separation resolution of the estimation technique. We assess the accuracy of this new framework by applying it to synthetic and experimentally obtained HARDI data. © 2011 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging
PublisherSpringer Nature
Pages538-549
Number of pages12
ISBN (Print)9783642220913
DOIs
StatePublished - 2011
Externally publishedYes

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