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

  • Fangxiang Jiao
  • Yaniv Gur
  • Chris R. Johnson
  • Sarang Joshi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6801)


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.


Homogeneous Polynomial Orientation Distribution Function Tensor Rank Separation Angle High Angular Resolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fangxiang Jiao
    • 1
  • Yaniv Gur
    • 1
  • Chris R. Johnson
    • 1
  • Sarang Joshi
    • 1
  1. 1.SCI InstituteUniversity of UtahSalt Lake CityUSA

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