Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training.

Journal: Magnetic resonance in medicine
Published Date: Oct 26, 2020

Abstract

PURPOSE: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training.

Authors

  • Ramin Jafari
    Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
  • Pascal Spincemaille
    Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
  • Jinwei Zhang
    Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
  • Thanh D Nguyen
    Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
  • Xianfu Luo
    Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
  • Junghun Cho
    Department of Biomedical Engineering, Cornell University, Ithaca, New York.
  • Daniel Margolis
    Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
  • Martin R Prince
    Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
  • Yi Wang
    Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

Keywords

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