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

Fergus Shone, Nishant Ravikumar, Toni Lassila, Michael MacRaild, Yongxing Wang, Zeike A. Taylor, Peter Jimack, Erica Dall’Armellina, Alejandro F. Frangi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings
EditorsAlejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab
PublisherSpringer Nature
Number of pages12
ISBN (Print)9783031340475
Publication statusPublished - 2023
Event28th International Conference on Information Processing in Medical Imaging, IPMI 2023 - San Carlos de Bariloche, Argentina
Duration: 18 Jun 202323 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13939 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Information Processing in Medical Imaging, IPMI 2023
CitySan Carlos de Bariloche


  • 4D-flow MRI
  • Physics-informed machine learning
  • Super-resolution


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