Assessing the methodology used to study the ascending aorta haemodynamics in bicuspid aortic valve. Issue 2 (11th June 2021)

• Record Type: Journal Article
• Authors: Edlin, Joy; Nowell, Justin; Arthurs, Christopher; Figueroa, Alberto; Jahangiri, Marjan
• Is Part Of: European heart journal. Volume 2:Issue 2(2021)
• Page Start: 271
• Page End: 278
• Publication Date: 2021-06-11
• Subjects: Aortic valve -- Computational fluid dynamics; Medical informatics -- Periodicals; Medical technology -- Periodicals; Cardiovascular system -- Periodicals; 616.100284
• DOI: 10.1093/ehjdh/ztab022
• Languages: English
• ISSNs: 2634-3916

Abstract

Abstract: Aims : Modern imaging techniques provide evermore-detailed anatomical and physiological information for use in computational fluid dynamics to predict the behaviour of physiological phenomena. Computer modelling can help plan suitable interventions. Our group used magnetic resonance imaging and computational fluid dynamics to study the haemodynamic variables in the ascending aorta in patients with bicuspid aortic valve before and after isolated tissue aortic valve replacement. Computer modelling requires turning a physiological model into a mathematical one, solvable by equations that undergo multiple iterations in four dimensions. Creating these models involves several steps with manual inputs, making the process prone to errors and limiting its inter- and intra-operator reproducibility. Despite these challenges, we created computational models for each patient to study ascending aorta blood flow before and after surgery. Methods and results : Magnetic resonance imaging provided the anatomical and velocity data required for the blood flow simulation. Patient-specific in- and outflow boundary conditions were used for the computational fluid dynamics analysis. Haemodynamic variables pertaining to blood flow pattern and derived from the magnetic resonance imaging data were calculated. However, we encountered problems in our multi-step methodology, most notably processing the flow data. This meant that other variables requiring computation with computational fluid dynamics could not be calculated. Conclusion : Creating a model for computational fluid dynamics analysis is as complex as the physiology under scrutiny. We discuss some of the difficulties associated with creating such models, along with suggestions for improvements in order to yield reliable and beneficial results.