Improving Image Quality and Diagnostic Performance of CCTA in Patients with Challenging Heart Rate Conditions using a Deep Learning-based Motion Correction Algorithm.

Journal: Current medical imaging
PMID: 39257151

Abstract

OBJECTIVE: Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep learning-based motion correction algorithm (MCA) on motion artifacts in patients with challenging HR conditions, focusing on image quality and diagnostic performance of CCTA.

Authors

  • Ziwei Wang
    School of Information Technology and Electrical Engineering, University of Queensland, Brisbane Australia.
  • Li Bao
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Sihua Zhong
    Research Center Institute, United Imaging Healthcare, Shanghai, China.
  • Fan Xiong
  • Linze Zhong
    Department of Radiology, Shangjin Hospital, West China Hospital, Sichuan University, Chengdu, China.
  • Daojin Wang
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Tao Shuai
    Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
  • Min Wu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.

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