A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography.

Journal: Magnetic resonance imaging
Published Date: Apr 27, 2020

Abstract

PURPOSE: To enable fast reconstruction of undersampled motion-compensated whole-heart 3D coronary magnetic resonance angiography (CMRA) by learning a multi-scale variational neural network (MS-VNN) which allows the acquisition of high-quality 1.2 × 1.2 × 1.2 mm isotropic volumes in a short and predictable scan time.

Authors

  • Niccolo Fuin
    School of Biomedical Engineering and Imaging Sciences, King's College London, UK. Electronic address: niccolo.fuin@kcl.ac.uk.
  • Aurelien Bustin
  • Thomas Küstner
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.
  • İlkay Öksüz
    İstanbul Technical University Faculty of Engineering, Department of Computer Engineering, İstanbul, Türkiye.
  • James Clough
    School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
  • Andrew P King
    Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom. Electronic address: andrew.king@kcl.ac.uk.
  • Julia A Schnabel
    Division of Imaging Sciences and Biomedical Engineering, King's College London, UK.
  • René M Botnar
    School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
  • Claudia Prieto
    School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.

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