Adjugate diffusion tensors for geodesic tractography in white matter

A. Fuster, T.C.J. Dela Haije, A. Tristán-Vega, B.R. Plantinga, C.-F. Westin, L.M.J. Florack

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Abstract

One of the approaches in diffusion tensor imaging is to consider a Riemannian metric given by the inverse diffusion tensor. Such a metric is used for geodesic tractography and connectivity analysis in white matter. We propose a metric tensor given by the adjugate rather than the previously proposed inverse diffusion tensor. The adjugate metric can also be employed in the sharpening framework. Tractography experiments on synthetic and real brain diffusion data show improvement for high-curvature tracts and in the vicinity of isotropic diffusion regions relative to most results for inverse (sharpened) diffusion tensors, and especially on real data. In addition, adjugate tensors are shown to be more robust to noise. Keywords: Riemannian geometry; Geodesic tractography; Diffusion tensor imaging; Brownian motion; Diffusion generator; Sharpened diffusion tensor
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalJournal of Mathematical Imaging and Vision
Volume54
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016

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Keywords

  • Brownian motion
  • Diffusion generator
  • Diffusion tensor imaging
  • Geodesic tractography
  • Riemannian geometry
  • Sharpened diffusion tensor

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