Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

Journal: BMC medical informatics and decision making
PMID: 39696218

Abstract

BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. This study investigated the feasibility of a deep learning iterative reconstruction algorithm (Deep IR) in reducing the radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube voltage and current.

Authors

  • Shumeng Zhu
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China.
  • Baoping Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
  • Qian Tian
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China.
  • Ao Li
    Beijing University of Chinese Medicine, Beijing, China.
  • Zhe Liu
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
  • Wei Hou
    Institute of Special Animal and Plant Sciences, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • Wenzhe Zhao
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi province, China.
  • Xin Huang
    Department of ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
  • Yao Xiao
    School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China; Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Yiming Wang
    Teaching Resource Information Service Center, Changchun Institute of Education, Changchun, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Yuhang Li
    Zhejiang Key Laboratory of Excited-State Energy Conversion and Energy Storage, Department of Chemistry, Zhejiang University, Hangzhou 310058, China.
  • Jian Yang
    Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.
  • Chao Jin
    Department of General Surgery, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.

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