The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To explore the impact of deep learning reconstruction (DLR) on image quality and machine learning-based coronary CT angiography (CTA)-derived fractional flow reserve (CT-FFR) values.

Authors

  • Cheng Xu
    School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, 2052 Sydney, Australia.
  • Min Xu
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Jing Yan
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
  • Yan-Yu Li
    Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
  • Yan Yi
    Departments of Radiology, Peking Union Medical College Hospital, Beijing.
  • Yu-Bo Guo
    Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
  • Ming Wang
    Brain center, Zhejiang Hospital, Hangzhou, China.
  • Yu-Mei Li
    Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
  • Zheng-Yu Jin
    Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
  • Yi-Ning Wang
    Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China. wangyining@pumch.cn.