Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: May 4, 2022

Abstract

PURPOSE: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).

Authors

  • Abolfazl Mehranian
    GE Healthcare, Big Data Institute, University of Oxford, Oxford, UK.
  • Scott D Wollenweber
    GE Healthcare, Waukesha, WI, USA.
  • Matthew D Walker
    Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford, UK.
  • 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.
  • Fotis Kotasidis
    GE Healthcare, Zurich, Switzerland.
  • Kuan-Hao Su
    Case Center for Imaging Research, Case Western Reserve University, Cleveland, OH, United States of America. Department of Radiology, Case Western Reserve University, Cleveland, OH, United States of America.
  • 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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