Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

Journal: Journal of applied clinical medical physics
PMID: 37485892

Abstract

PURPOSE: To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological and functional evaluation.

Authors

  • Xiaoling Yao
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Sihua Zhong
    Research Center Institute, United Imaging Healthcare, Shanghai, China.
  • Maolan Xu
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Guozhi Zhang
    Department of Radiology, KU Leuven University Hospitals Leuven, Leuven, Belgium.
  • Yuan Yuan
    Department of Geriatrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
  • Tao Shuai
    Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
  • Zhenlin Li
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China.

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