Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute.

Journal: Magnetic resonance in medicine
Published Date: Jul 9, 2021

Abstract

PURPOSE: To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute.

Authors

  • Thomas Küstner
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.
  • Camila Munoz
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom.
  • Alina Psenicny
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom.
  • Aurelien Bustin
  • Niccolo Fuin
    School of Biomedical Engineering and Imaging Sciences, King's College London, UK. Electronic address: niccolo.fuin@kcl.ac.uk.
  • Haikun Qi
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, London, United Kingdom.
  • Radhouene Neji
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.
  • Karl Kunze
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Reza Hajhosseiny
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.
  • Claudia Prieto
    School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.
  • Rene Botnar
    School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.

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