@inbook{89b228da28f64c2eb845ae6ebe187a3b,
title = "Deep Physics-Informed Super-Resolution of Cardiac 4D-Flow MRI",
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 × and 10 × respectively, using a signal-to-noise ratio of 7.",
keywords = "4D-flow MRI, Physics-informed machine learning, Super-resolution",
author = "Fergus Shone and Nishant Ravikumar and Toni Lassila and Michael MacRaild and Yongxing Wang and Taylor, {Zeike A.} and Peter Jimack and Erica Dall{\textquoteright}Armellina and Frangi, {Alejandro F.}",
note = "Funding Information: This work was partially supported by the EPSRC Centre for Doctoral Training in Fluid Dynamics (EP/L01615X/1) and the Royal Academy of Engineering Chair in Emerging Technologies (CiET1919/19). The computational work was undertaken on the UK National Tier-2 high performance computing service JADE2 (EP/T022205/1). Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 28th International Conference on Information Processing in Medical Imaging, IPMI 2023 ; Conference date: 18-06-2023 Through 23-06-2023",
year = "2023",
month = jun,
day = "8",
doi = "10.1007/978-3-031-34048-2_39",
language = "English",
isbn = "9783031340475",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "511--522",
editor = "Alejandro Frangi and {de Bruijne}, Marleen and Demian Wassermann and Nassir Navab",
booktitle = "Lecture Notes in Computer Science",
address = "United States",
}