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
4D-flow magnetic resonance imaging (MRI) provides non-invasive blood flow reconstructions in the heart. However, low spatio-temporal resolution and significant noise artefacts hamper the accuracy of derived haemodynamic quantities. We propose a physics-informed super-resolution approach to address these shortcomings and uncover hidden solution fields. We demonstrate the feasibility of the model through two synthetic studies generated using computational fluid dynamics. The Navier-Stokes equations and no-slip boundary condition on the endocardium are weakly enforced, regularising model predictions to accommodate network training without high-resolution labels. We show robustness to each type of data degradation, achieving normalised velocity RMSE values of under 16% at extreme spatial and temporal upsampling rates of 16\times and 10\times respectively, using a signal-to-noise ratio of 7.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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): | oai:eprints.whiterose.ac.uk:197135 |