Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease.

Journal: BMC medical informatics and decision making
Published Date: Jul 1, 2025

Abstract

OBJECTIVE: Deep learning image reconstruction (DLIR) technology effectively improves the image quality while maintaining spatial resolution. The impact of DLIR on the quantification of coronary artery calcium (CAC) is still unclear. The purpose of this study was to investigate the effect of DLIR on the quantification of coronary calcium in high-risk populations.

Authors

  • Lijuan Zhu
    Zhengzhou University of Aeronautics, Henan, Zhengzhou 450046, China.
  • Xiaomeng Shi
    From the Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, 100 Haining Rd, Shanghai 200080, China (B.J., N.L., X.X.); CT Imaging Research Center, GE Healthcare China, Shanghai, China (X.S., S.Z., J.L.); and Departments of Epidemiology (G.H.d.B.) and Radiology (R.V.), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Lusong Tang
    People's Hospital of Ningxia Hui Autonomous Region to People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, 301 Zhengyuan North Street, Jinfeng District, Yinchuan, Ningxia, China.
  • Haruhiko Machida
    Department of Radiology, Kyorin University School of Medicine, Tokyo, Japan.
  • Lili Yang
    Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, University of South China, Hengyang 421001, China.
  • Meixiang Ma
    People's Hospital of Ningxia Hui Autonomous Region to People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, 301 Zhengyuan North Street, Jinfeng District, Yinchuan, Ningxia, China.
  • Ruoshui Ha
    People's Hospital of Ningxia Hui Autonomous Region to People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, 301 Zhengyuan North Street, Jinfeng District, Yinchuan, Ningxia, China.
  • Yun Shen
    State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China.
  • Fang Wang
    Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China.
  • Dazhi Chen
    People's Hospital of Ningxia Hui Autonomous Region to People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, 301 Zhengyuan North Street, Jinfeng District, Yinchuan, Ningxia, China.

Keywords

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