Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy.

Journal: BMC medical imaging
Published Date: Sep 9, 2024

Abstract

BACKGROUND: To evaluate the clinical performance of two deep learning methods, one utilizing real clinical pairs and the other utilizing simulated datasets, in enhancing image quality for two-dimensional (2D) fast whole-body scintigraphy (WBS).

Authors

  • Na Qi
    College of Life Science, Capital Normal University, Beijing, 100048, China.
  • Boyang Pan
    RadioDynamic Healthcare, Shanghai, People's Republic of China.
  • Qingyuan Meng
    Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, 200120, Shanghai, People's Republic of China.
  • Yihong Yang
    Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, 200120, Shanghai, People's Republic of China.
  • Jie Ding
    State Key Laboratory of Respiratory Disease, Joint School of Life Sciences, Guangzhou Chest Hospital, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, China.
  • Zengbei Yuan
    College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
  • Nan-Jie Gong
    Vector Lab for Intelligent Medical Imaging and Neural Engineering, International Innovation Center of Tsinghua University, Shanghai, China.
  • Jun Zhao

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