Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

Abstract

Authors

• Hua Jiang
  Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China. Electronic address: hua.jiang@traumabank.org.
• Songbing Qin
  Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
• Lecheng Jia
  Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China. lecheng.jia@cri-united-imaging.com.
• Ziquan Wei
  Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, China.
• Weiqi Xiong
  Shanghai United Imaging Healthcare Co., Ltd. Shanghai, Shanghai, China.
• Wentao Xu
  Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 790-784, Republic of Korea.
• Wei Gong
  State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
• Wei Zhang
  The First Affiliated Hospital of Nanchang University, Nanchang, China.
• Liqin Yu
  Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.

Keywords

External Resources