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 × and 10 × respectively, using a signal-to-noise ratio of 7.
CITATION STYLE
Shone, F., Ravikumar, N., Lassila, T., MacRaild, M., Wang, Y., Taylor, Z. A., … Frangi, A. F. (2023). Deep Physics-Informed Super-Resolution of Cardiac 4D-Flow MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13939 LNCS, pp. 511–522). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34048-2_39
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