Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

Journal: Magnetic resonance in medicine
Published Date: May 17, 2018

Abstract

PURPOSE: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning.

Authors

  • Florian Knoll
    Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA.
  • Kerstin Hammernik
    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.
  • Thomas Pock
    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.

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