Learning a variational network for reconstruction of accelerated MRI data.

Journal: Magnetic resonance in medicine
Published Date: Nov 8, 2017

Abstract

PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.

Authors

  • Kerstin Hammernik
    Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
  • Teresa Klatzer
    Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
  • Erich Kobler
    Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
  • Michael P Recht
    Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA.
  • Daniel K Sodickson
    Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA.
  • Thomas Pock
    Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
  • Florian Knoll
    Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA.

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