Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging.

Journal: Computer methods and programs in biomedicine
PMID:

Abstract

INTRODUCTION: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.

Authors

  • Matteo Ferrante
  • Marianna Inglese
    Department of Biomedicine and Prevention, University of Rome, Tor Vergata, Rome, Italy. Electronic address: marianna.inglese@uniroma2.it.
  • Ludovica Brusaferri
    Athinoula A. Martinos Center For Biomedical Imaging, MGH and Harvard Medical School, Boston, MA, USA; Department of Computer Science and Informatics, School of Engineering, London South Bank University, London, UK.
  • Alexander C Whitehead
    Department of Computer Science, University College London, London, UK.
  • Lucia Maccioni
    Department of Information Engineering, University of Padua, Padua, Italy.
  • Federico E Turkheimer
    Centre for Neuroimaging Sciences, Institute of Psychology, Psychiatry and Neuroscience (IoPPN), King's College London, London, UK.
  • Maria A Nettis
    Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Valeria Mondelli
    Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Oliver Howes
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; H. Lundbeck UK, Ottiliavej 9 2500 Valby, Denmark; Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN.
  • Marco L Loggia
    Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.
  • Mattia Veronese
    Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Information Engineering, University of Padua, Padua, Italy.
  • Nicola Toschi
    Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Cracovia, 00133, Roma, RM, Italy; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA. Electronic address: toschi@med.uniroma2.it.