Feasibility of a deep learning algorithm to achieve the low-dose Ga-FAPI/the fast-scan PET images: a multicenter study.

Abstract

Authors

• Lin Liu
  Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory.
• Xingyu Chen
  Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China.
• Liwen Wan
  Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
• Na Zhang
  Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing, China.
• Ruibao Hu
  Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
• Wenbo Li
  Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Xianhu Hydrogen Valley, Foshan 528200, China.
• Shengping Liu
  Unisound AI Technology Co., Ltd., Beijing, China.
• Yan Zhu
  Department of Chemistry, Xixi Campus, Zhejiang University, Hangzhou, 310028, China. Electronic address: zhuyan@zju.edu.cn.
• Hua Pang
  Oil Crops Research Institute, Chinese Academy of Agricultural Sciences Wuhan 430062 China peiwuli@oilcrops.cn zhangqi521x@126.com +86-27-8681-2943 +86-27-8671-1839.
• Dong Liang
  Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.
• Yue Chen
  The College of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China.
• Zhanli Hu
  Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.

Keywords

External Resources