Motion artefact reduction in coronary CT angiography images with a deep learning method.

Journal: BMC medical imaging
Published Date: Oct 28, 2022

Abstract

BACKGROUND: The aim of this study was to investigate the ability of a pixel-to-pixel generative adversarial network (GAN) to remove motion artefacts in coronary CT angiography (CCTA) images.

Authors

  • Pengling Ren
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, People's Republic of China.
  • Yi He
    National Institutes for Food and Drug Control, 2 Tiantan Xili, Beijing 100050, China.
  • Yi Zhu
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Tingting Zhang
    Department of Environmental Science and Engineering, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China. Electronic address: zhangtt@mail.buct.edu.cn.
  • Jiaxin Cao
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China.
  • Zhenchang Wang
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Zhenghan Yang
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Keywords

External Resources

Popular Topics
Recent Journals