Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.

Journal: Biomedical engineering online
PMID:

Abstract

PURPOSE: The aim of this study is to convert low-dose PET (L-PET) images to full-dose PET (F-PET) images based on our Diffused Multi-scale Generative Adversarial Network (DMGAN) to offer a potential balance between reducing radiation exposure and maintaining diagnostic performance.

Authors

  • Xiang Yu
  • Daoyan Hu
    The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, China.
  • Qiong Yao
    Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
  • Yu Fu
    Molecular Diagnosis and Treatment Center for Infectious Diseases Dermatology Hospital Southern Medical University Guangzhou China.
  • Yan Zhong
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Mei Tian
    Huashan Hospital and Human Phenome Institute, Fudan University, Shanghai, China; tianmei@fudan.edu.cn hzhang21@zju.edu.cn.
  • Hong Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.