Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Feb 19, 2025

Abstract

PURPOSE: Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate organ segmentation using PET-only data remains challenging. This study develops and validates a deep learning model for multi-organ PET segmentation across varied imaging conditions and tracers, addressing critical needs for fully PET-based quantitative analysis.

Authors

  • Hanzhong Wang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Xiaoya Qiao
  • Wenxiang Ding
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Gaoyu Chen
    Department of Nuclear Medicine, Rui Jin Hospital, School of Medcine, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Ying Miao
    College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China.
  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Xiaohua Zhu
  • Zhaoping Cheng
    Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
  • Jiehua Xu
    Department of Nuclear Medicine, Zhuhai Clinical Medical College of Jinan University (Zhuhai People's Hospital), Zhuhai, China.
  • Biao Li
    Key Laboratory of Renewable Energy, Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China.
  • Qiu Huang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

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