Exploring Clinical and Computational Insights into Haemodynamic Modelling in Coarctation of the Aorta

Abstract

Coarctation of the aorta (CoA) is a congenital vascular narrowing that contributes to elevated blood pressure, abnormal wall stresses, and long-term cardiovascular risk. While computational fluid dynamics (CFD) offers high-fidelity insight into such haemodynamic disturbances, its clinical adoption remains limited by high computational cost, technical complexity, and reliance on advanced imaging such as 4D flow MRI. These limitations are especially pronounced in low-resource settings. This thesis addresses these barriers by developing the \textbf{Aorta CFD App}—an automated, open-source CFD pipeline designed to deliver personalised, turbulence-resolving CoA analysis in clinically accessible formats. The App integrates CT angiography (CTA) geometries and Doppler ultrasound-derived waveforms within a large eddy simulation (LES) framework, removing the dependence on MRI-based data. Its Python-based interface automates preprocessing, boundary condition assignment, meshing, and post-processing, significantly reducing user burden and technical entry barriers. To support time-sensitive workflows, a \textbf{bin-based phase-averaging strategy} was investigated to accelerate convergence in LES models. Segmenting each cardiac cycle into 50 bins reduced runtime by up to 60\% and storage by 30\%, while preserving over 95\% of kinetic energy and maintaining accuracy in turbulence metrics such as wall shear stress (WSS), oscillatory shear index (OSI), and turbulent kinetic energy (TKE). Though not embedded in the App, this method offers a generalizable approach to make LES viable for future clinical CFD. A focused \textbf{heart rate modulation study} (100–160 bpm) revealed how increasing HR amplifies TKE and systolic pressure gradients, impacting shear patterns and jet behaviour—key biomarkers in CoA treatment planning and beta-blocker therapy assessment. To evaluate accessibility, the App was deployed in 13 anonymised paediatric CoA cases from Cape Town, South Africa. Simulations were completed in under 12 hours on consumer-grade hardware by non-specialist users, confirming both the App’s usability and clinical robustness. A further \textbf{fidelity trade-off analysis} examined mesh resolution across five virtual repair states, revealing that while low-fidelity models replicate bulk flow in well-repaired cases, high-fidelity LES remains essential to resolve complex post-repair flow dynamics. Finally, this thesis proposes the integration of \textbf{multi-fidelity surrogate modelling} as a future extension—pairing fast simulations with high-fidelity data to enable real-time inference of pressure, velocity, and shear for decision support. In summary, this work delivers a scalable, accessible framework for personalised cardiovascular simulation in resource-constrained settings. By advancing interoperability, automation, and adaptive modelling, the Aorta CFD App contributes to democratising precision haemodynamics and translating CFD into clinical impact where it is most needed.

Date of Award	12 Aug 2025
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Alistair Revell (Main Supervisor), Amir Keshmiri (Co Supervisor) & Bernard Keavney (Co Supervisor)