Tracer-Separator: A Deep Learning Model for Brain PET Dual-Tracer ( 18 F-FDG and Amyloid) Separation.

Journal: Clinical nuclear medicine
PMID: 39468375

Abstract

INTRODUCTION: Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, enhancing diagnostic accuracy and patient comfort. Through a transformer-based deep learning, this study underscores the potential of advanced imaging techniques to streamline diagnosis and improve patient outcomes.

Authors

  • Amirhossein Sanaat
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Yiyi Hu
    Institute of Veterinary Medicine, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China.
  • Cecilia Boccalini
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland. cecilia.boccalini@unige.ch.
  • Yazdan Salimi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Zahra Mansouri
    Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Eliluane Pirazzo Andrade Teixeira
    From the 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.
  • 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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