PETFormer-SCL: a supervised contrastive learning-guided CNN-transformer hybrid network for Parkinsonism classification from FDG-PET.

Journal: Annals of nuclear medicine
Published Date: Jul 14, 2025

Abstract

PURPOSE: Accurate differentiation of Parkinsonism subtypes-including Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP)-is essential for clinical prognosis and treatment planning. However, this remains a major challenge due to overlapping symptomatology and high inter-individual variability in cerebral glucose metabolism patterns observed on fluorodeoxyglucose positron emission tomography (FDG-PET).

Authors

  • Shaoyou Wu
    Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China.
  • Chenyang Li
  • Jiaying Lu
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
  • Jingjie Ge
    PET Center, Huashan Hospital, Fudan University, No 518, East Wuzhong Road, Xuhui District, Shanghai, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Chuantao Zuo
  • Zhilin Zhang
    State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
  • Jiehui Jiang
    Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.

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