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Longitudinal Change Detection: Inference on the Diffusion Tensor Along White-Matter Pathways

  • Antoine Grigis
  • Vincent Noblet
  • Fréderic Blanc
  • Fabrice Heitz
  • Jérome de Seze
  • Jean-Paul Armspach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

Diffusion tensor magnetic resonance imaging (DT-MRI) tractography allows to probe brain connections in vivo. This paper presents a change detection framework that relies on white-matter pathways with application to neuromyelitis optica (NMO). The objective is to detect global or local fiber diffusion property modifications between two longitudinal DT-MRI acquisitions of a patient. To this end, estimation and testing tools on tensors along the white-matter pathways are considered. Two tests are implemented: a pointwise test that compares at each sampling point of the fiber bundle the tensor populations of the two exams in the cross section of the bundle and a fiberwise test that compares paired tensors along all the fiber bundle. Experiments on both synthetic and real data highlight the benefit of considering fiber based statistical tests compared to the standard voxelwise strategy.

Keywords

Expand Disability Status Scale Neuromyelitis Optica Generalize Likelihood Ratio Test Diffusion Tensor Magnetic Resonance Imaging Reference Tract 
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

  • Antoine Grigis
    • 1
    • 2
  • Vincent Noblet
    • 1
  • Fréderic Blanc
    • 2
  • Fabrice Heitz
    • 1
  • Jérome de Seze
    • 2
  • Jean-Paul Armspach
    • 2
  1. 1.University of Strasbourg, LSIIT, UMR 7005, CNRSFrance
  2. 2.University of Strasbourg, LINC-IPB, UMR 7237, CNRSFrance

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