An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.

Journal: Magnetic resonance in medicine
Published Date:

Abstract

PURPOSE: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.

Authors

  • Kalina P Slavkova
    Department of Physics, The University of Texas, Austin, Texas, USA.
  • Julie C DiCarlo
    The Oden Institute for Computational Engineering and Sciences, The University of Texas, Austin, Texas, USA.
  • Viraj Wadhwa
    Chandra Family Department of Electrical and Computer Engineering, The University of Texas, Austin, Texas, USA.
  • Sidharth Kumar
    Chandra Family Department of Electrical and Computer Engineering, The University of Texas, Austin, Texas, USA.
  • Chengyue Wu
    The Oden Institute for Computational Engineering and Sciences, The University of Texas, Austin, Texas, USA.
  • John Virostko
    Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, 1701 Trinity St., Stop C0200, Austin, TX, 78712, USA. jack.virostko@austin.utexas.edu.
  • Thomas E Yankeelov
    Department of Biomedical Engineering, The University of Texas at Austin, TX 78712, USA.
  • Jonathan I Tamir
    Subtle Medical Inc., Menlo Park, CA, USA.