A Novel Measure of Fractional Anisotropy Based on the Tensor Distribution Function

  • Authors:

  • Liang Zhan;Alex D. Leow;Siwei Zhu;Marina Barysheva;Arthur W. Toga;Katie L. Mcmahon;Greig I. Zubicaray;Margaret J. Wright;Paul M. Thompson

  • Affiliations:

  • Laboratory of Neuroimaging, Dept. of Neurology, University of California, Los Angeles, USA;Laboratory of Neuroimaging, Dept. of Neurology, University of California, Los Angeles, USA and Department of Psychiatry, University of Illinois Medical Center at Chicago, USA;Dept. of Mathematics, University of California, Los Angeles, USA;Laboratory of Neuroimaging, Dept. of Neurology, University of California, Los Angeles, USA;Laboratory of Neuroimaging, Dept. of Neurology, University of California, Los Angeles, USA;Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Queensland Institute of Medical Research, Brisbane, Australia;Laboratory of Neuroimaging, Dept. of Neurology, University of California, Los Angeles, USA

  • Venue:
  • MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
  • Year:
  • 2009

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Abstract

Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.