Assessment of Image Quality of Coronary Computed Tomography Angiography in Obese Patients by Comparing Deep Learning Image Reconstruction With Adaptive Statistical Iterative Reconstruction Veo.

Journal: Journal of computer assisted tomography
Published Date:

Abstract

OBJECTIVE: The aim of the study was to evaluate the image quality of coronary computed tomography (CT) angiography (CCTA) in obese patients by using deep learning image reconstruction (DLIR) in comparison with adaptive statistical iterative reconstruction Veo (ASiR-V).

Authors

  • Hongwei Wang
    Department of Oncological Surgery, Harbin Medical University Cancer Hospital, Harbin, 150000, Heilongjiang Province, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Zhen Zhou
    Deepwise Healthcare, Beijing 100080, China.
  • Yifeng Gao
    From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University.
  • Kairui Bo
    From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University.
  • Min Yu
    From the Division of Laboratory Medicine, Department of Pathology, University of Virginia School of Medicine and Health System, Charlottesville. Dr Yu is currently located in the Department of Pathology and Laboratory Medicine, University of Kentucky Medical Center, Lexington.
  • Zhonghua Sun
    Beijing Univ. of Technology, China.
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.