Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.

Journal: European radiology
Published Date: Oct 18, 2019

Abstract

OBJECTIVE: To classify motion-induced blurred images of calcified coronary plaques so as to correct coronary calcium scores on nontriggered chest CT, using a deep convolutional neural network (CNN) trained by images of motion artifacts.

Authors

  • Yaping Zhang
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080 China.
  • Niels R van der Werf
    Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
  • Beibei Jiang
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080 China.
  • Robbert van Hamersvelt
    Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
  • Marcel J W Greuter
    University Medical Center Groningen, Radiology Department, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
  • Xueqian Xie
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080 China.

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