Diffusion of Fiber Orientation Distribution Functions with a Rotation-Induced Riemannian Metric

  • Junning Li
  • Yonggang Shi
  • Arthur W. Toga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)


Advanced diffusion weighted MR imaging allows non-invasive study on the structural connectivity of human brains. Fiber orientation distributions (FODs) reconstructed from diffusion data are a popular model to represent crossing fibers. For this sophisticated image representation of connectivity, classical image operations such as smoothing must be redefined. In this paper, we propose a novel rotation-induced Riemannian metric for FODs, and introduce a weighted diffusion process for FODs regarding this Riemannian manifold. We show how this Riemannian manifold can be used for smoothing, interpolation and building image-pyramids, yielding more accurate or intuitively more reasonable results than the linear or the unit hyper-sphere manifold.


Riemannian Manifold Spherical Function Linear Manifold Image Pyramid Fiber Orientation Distribution 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Junning Li
    • 1
  • Yonggang Shi
    • 1
  • Arthur W. Toga
    • 1
  1. 1.Laboratory of Neuro Imaging (LONI), Institute for Neuroimaging and Informatics (INI)Keck School of Medicine of USCUSA

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