Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

Journal: Medical physics
Published Date: May 6, 2024

Abstract

BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties.

Authors

  • Kaushik Dutta
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri, USA.
  • Richard Laforest
    Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Jingqin Luo
    Department of Surgery, Washington University in St. Louis, St. Louis, MO, 63110, USA.
  • Abhinav K Jha
    Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States of America.
  • Kooresh I Shoghi
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri, USA.

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