Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning-based filter using convolutional neural network.

Journal: European radiology
Published Date: Nov 1, 2020

Abstract

OBJECTIVES: To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium-enhanced multi-arterial phase MRI of the liver.

Authors

  • M-L Kromrey
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan. marie-luise.kromrey@uni-greifswald.de.
  • D Tamada
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • H Johno
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • S Funayama
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • N Nagata
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • S Ichikawa
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • J-P Kühn
    Institute of Diagnostic and Interventional Radiology, University Medicine, Carl-Gustav Carus University, Dresden, Germany.
  • H Onishi
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • U Motosugi
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.

Keywords

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