Machine Learning in Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects.

Journal: Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Published Date: Sep 25, 2020

Abstract

This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algorithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward.

Authors

  • Katherine Zukotynski
    Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; and zukotynk@mcmaster.ca.
  • Vincent Gaudet
    Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada.
  • Carlos F Uribe
    Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada.
  • Sulantha Mathotaarachchi
    Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
  • Kenneth C Smith
    Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Pedro Rosa-Neto
    Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada. Electronic address: pedro.rosa@mcgill.ca.
  • François Bénard
    Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada.
  • Sandra E Black
    Institute of Medical Science, University of Toronto, Toronto, ON Canada.

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