Deep learning-based triple-tracer brain PET scanning in a single session: A simulation study using clinical data.

Journal: NeuroImage
Published Date: Apr 30, 2025

Abstract

OBJECTIVES: Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often require significant delays between tracer injections, leading to physiological changes and noise interference. Recent advancements, including multi-tracer compartment modeling and machine learning, provide promising solutions. This study explores the deep learning (DL)-based single-session triple-tracer brain PET imaging protocol, aiming at simplifying multi-tracer PET imaging, while reducing radiation exposure.

Authors

  • Yiyi Hu
    Institute of Veterinary Medicine, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China.
  • Amirhossein Sanaat
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Gregory Mathoux
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Pirazzo Andrade Teixeira Eliluane
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Geneva, Switzerland.
  • Valentina Garibotto
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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