4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics

Ferdian, E, Suinesiaputra, A orcid.org/0000-0003-1165-458X, Dubowitz, DJ et al. (4 more authors) (2020) 4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics. Frontiers in Physics, 8. 138. ISSN 2296-424X

Abstract

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Copyright, Publisher and Additional Information: Copyright © 2020 Ferdian, Suinesiaputra, Dubowitz, Zhao, Wang, Cowan and Young. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: 4D flow MRI, super resolution network, SRResNet, deep learning, computational fluid dynamics, CFD, velocity fields
Dates:
  • Accepted: 8 April 2020
  • Published (online): 4 May 2020
  • Published: 4 May 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 02 Jul 2020 13:18
Last Modified: 02 Jul 2020 13:18
Status: Published
Publisher: Frontiers Media
Identification Number: https://doi.org/10.3389/fphy.2020.00138
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