Improving image quality and diagnostic performance using deep learning image reconstruction in 100-kVp CT enterography for patients with wide-range body mass index.

Journal: European journal of radiology
Published Date:

Abstract

OBJECTIVE: To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion detection in 100-kVp CT enterography (CTE) for patients with wide-range body mass index (BMI).

Authors

  • Yan Luo
    School of Public Health and Management, Research Center for Medicine and Social Development, Innovation Center for Social risk Governance in Health, Chongqing Medical University, Chongqing 400016, China.
  • Youfa Tang
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Suping Chen
    GE Healthcare, Computed Tomography Research Center, Beijing, 100176, People's Republic of China.
  • Hao Tang
    Department of Urology, Eastern Theater General Hospital of Chinese People's Liberation Army, Nanjing, Jiangsu 210000, China.
  • Yiqi Cheng
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Fan Zhang
    Department of Anesthesiology, Bishan Hospital of Chongqing Medical University, Chongqing, China.
  • Yaqi Shen
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: yqshen@hust.edu.cn.
  • Qiuxia Wang
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: wangqiuxia@hust.edu.cn.