Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.

Journal: Magnetic resonance imaging
Published Date: Oct 1, 2019

Abstract

PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology.

Authors

  • Vishwesh Nath
    Computer Science, Vanderbilt University, Nashville, TN, USA.
  • Kurt G Schilling
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Prasanna Parvathaneni
  • Colin B Hansen
    Computer Science, Vanderbilt University, Nashville, TN, USA.
  • Allison E Hainline
    Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Yuankai Huo
    Vanderbilt University, Nashville, TN 37212, USA.
  • Justin A Blaber
    Computer Science, Vanderbilt University, Nashville, TN, USA.
  • Ilwoo Lyu
    Computer Science, Vanderbilt Universitay, Nashville, TN, USA. Electronic address: lwoo.Lyu@vanderbilt.edu.
  • Vaibhav Janve
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Yurui Gao
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Iwona Stepniewska
    Psychology, Vanderbilt University, Nashville, TN, USA.
  • Adam W Anderson
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Bennett A Landman
    Vanderbilt University, Nashville TN 37235, USA.

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