Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Feb 18, 2025

Abstract

AIM: To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, reconstructed using BSREM algorithm, towards ToF images.

Authors

  • Abolfazl Mehranian
    GE Healthcare, Big Data Institute, University of Oxford, Oxford, UK.
  • Scott D Wollenweber
    GE Healthcare, Waukesha, WI, USA.
  • Kevin M Bradley
    Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK.
  • Patrick A Fielding
    Department of Radiology, University Hospital of Wales, Cardiff, UK.
  • Martin Huellner
    Zurich University Hospital, Zurich, Switzerland.
  • Andrei Iagaru
    Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Dr, Stanford, CA, 94305, USA.
  • Meghi Dedja
    Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford, UK.
  • Theodore Colwell
    GE HealthCare, Waukesha, USA.
  • Fotis Kotasidis
    GE Healthcare, Zurich, Switzerland.
  • Robert Johnsen
    GE Healthcare, Waukesha, WI, USA.
  • Floris P Jansen
    GE Healthcare, Waukesha, WI, USA.
  • Daniel R McGowan
    Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford, UK. Daniel.McGowan@oncology.ox.ac.uk.

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