Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.

Journal: Radiation oncology (London, England)
Published Date: Oct 14, 2021

Abstract

OBJECTIVE: To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.

Authors

  • Liugang Gao
    Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, 213003, China.
  • Kai Xie
    National Demonstration Center for Experimental Electrical and Electronic Education, Yangtze University, Jingzhou 434023, China. 500646@yangtzeu.edu.cn.
  • Xiaojin Wu
    Oncology Department, Xuzhou No.1 People's Hospital, Xuzhou, 221000, China.
  • Zhengda Lu
    Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, 213003, China.
  • Chunying Li
    Xuzhou University of Technology, Xuzhou, China.
  • Jiawei Sun
    Department of Bioengineering, Stanford University, CA, 94305, USA.
  • Tao Lin
  • Jianfeng Sui
    Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, 213003, China.
  • Xinye Ni
    Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, 213003, China. nxy@njmu.edu.cn.

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