Enhancing F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 40312739

Abstract

BACKGROUND: As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting lesion diagnostics. It is crucial to identify methods that ensure consistent diagnostic accuracy and maintain image quality. Deep progressive learning (DPL) algorithm, an Artificial Intelligence(AI)-based PET reconstruction technique, offers a promising solution.

Authors

  • Zhihao Chen
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
  • Hongxing Yang
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
  • Ming Qi
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
  • Wen Chen
    School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan, China.
  • Fei Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Shaoli Song
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. shaoli-song@163.com.
  • Jianping Zhang
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. zhangjianping@fudan.edu.cn.

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