Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Jan 1, 2022

Abstract

BACKGROUND: Deep learning (DL) has achieved great success in medical imaging and could be utilized for the non-invasive calculation of fractional flow reserve (FFR) from coronary computed tomographic angiography (CCTA) (CT-FFR).

Authors

  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Hong Qiu
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China.
  • Zhihui Hou
    Department of Radiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China.
  • Jianfeng Zheng
    Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, USA.
  • Jianan Li
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China.
  • Youbing Yin
    Department of Engineering, CuraCloud Corporation, Seattle, WA, USA.
  • Runlin Gao
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China.

Keywords

External Resources

Popular Topics
Recent Journals