Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy.

Journal: Seminars in radiation oncology
Published Date: Oct 1, 2022

Abstract

Quantitative magnetic resonance imaging (qMRI) has been shown to provide many potential advantages for personalized adaptive radiotherapy (RT). Deep learning models have proven to increase efficiency, robustness and speed for different qMRI tasks. Therefore, this article discusses the current state-of-the-art and potential future opportunities as well as challenges related to the use of deep learning in qMRI for target contouring, quantitative parameter estimation and also the generation of synthetic computerized tomography (CT) data based on MRI in personalized RT.

Authors

  • Oliver J Gurney-Champion
    Joint Department of Physics, The Institute of Cancer Research, London, United Kingdom.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Kathrine Røe Redalen
    Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway.
  • Daniela Thorwarth
    Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, a partnership between DKFZ and University Hospital Tübingen, Germany; Cluster of Excellence "Machine Learning: New Perspectives for Science", University of Tübingen, Germany.

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