Deep learning-based dynamic PET parametric K image generation from lung static PET.

Journal: European radiology
Published Date: Nov 18, 2022

Abstract

OBJECTIVES: PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric K provides better quantification and improve specificity for cancer detection. However, parametric imaging is difficult to implement clinically due to the long acquisition time (~ 1 h). We propose a dynamic parametric imaging method based on conventional static PET using deep learning.

Authors

  • Haiyan Wang
    College of Chemistry and Material Science, Shandong Agricultural University, Tai'an 271018, PR China.
  • Yaping Wu
    Department of Imaging, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Zhenxing Huang
    Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.
  • Zhicheng Li
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
  • Na Zhang
    Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing, China.
  • Fangfang Fu
    Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
  • Nan Meng
  • Haining Wang
    Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518045, China.
  • Yun Zhou
    MOE Key Lab of Environmental and Occupational Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430030, China.
  • Yongfeng Yang
    Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Dong Liang
    Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.
  • Hairong Zheng
    Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.
  • Greta S P Mok
    Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao SAR, China.
  • Meiyun Wang
  • Zhanli Hu
    Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.

Keywords

External Resources

Popular Topics
Recent Journals