Self-supervised neural network for Patlak-based parametric imaging in dynamic [F]FDG total-body PET.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 39621094

Abstract

PURPOSE: The objective of this study is to generate reliable K parametric images from a shortened [F]FDG total-body PET for clinical applications using a self-supervised neural network algorithm.

Authors

  • Wenjian Gu
    Faculty of Computing, Harbin Institute of Technology, Harbin, China.
  • Zhanshi Zhu
    Faculty of Computing, Harbin Institute of Technology, Harbin, China.
  • Ze Liu
    College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, Shaanxi, China.
  • Yihan Wang
    Vanderbilt University Medical Center, Nashville TN 37232, USA.
  • Yanxiao Li
    United Imaging Healthcare Technology Group Co., Ltd, Shanghai, China.
  • Tianyi Xu
    Deparement of Anesthesia, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
  • Weiping Liu
  • Gongning Luo
  • Kuanquan Wang
  • 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.

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