Deep Physics-Informed Super-Resolution of Cardiac 4D-Flow MRI

Shone, F, Ravikumar, N, Lassila, T orcid.org/0000-0001-8947-1447 et al. (6 more authors) (2023) Deep Physics-Informed Super-Resolution of Cardiac 4D-Flow MRI. In: Information Processing in Medical Imaging. Information Processing in Medical Imaging (IPMI 2023), 18-23 Jun 2023, San Carlos de Bariloche, Argentina. Lecture Notes in Computer Science . Springer , pp. 511-522. ISBN 978-3-031-34047-5

Abstract

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Item Type: Proceedings Paper
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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the conference paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-34048-2_39

Keywords: Physics-informed machine learning; 4D-flow MRI; Super-resolution
Dates:
  • Published: 8 June 2023
  • Published (online): 8 June 2023
  • Accepted: 7 March 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Funding Information:
Funder
Grant number
Royal Academy of Engineering
CiET1819\19
Depositing User: Symplectic Publications
Date Deposited: 13 Mar 2023 08:33
Last Modified: 08 Jun 2024 00:13
Status: Published
Publisher: Springer
Series Name: Lecture Notes in Computer Science
Identification Number: 10.1007/978-3-031-34048-2_39
Open Archives Initiative ID (OAI ID):

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