Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics. (20th September 2019)

• Record Type: Journal Article
• Authors: Hoeijmakers, M.J.M.M.; Silva Soto, D.A.; Waechter-Stehle, I.; Kasztelnik, M.; Weese, J.; Hose, D.R.; de Vosse, F.N. van
• Is Part Of: Journal of biomechanics. Volume 94(2019)
• Page Start: 49
• Page End: 58
• Publication Date: 2019-09-20
• Subjects: Aortic valve stenosis -- Heart valve disease -- Hemodynamics -- Computational fluid dynamics -- Patient-specific; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2019.07.010
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Aortic valve stenosis is associated with an elevated left ventricular pressure and transaortic pressure drop. Clinicians routinely use Doppler ultrasound to quantify aortic valve stenosis severity by estimating this pressure drop from blood velocity. However, this method approximates the peak pressure drop, and is unable to quantify the partial pressure recovery distal to the valve. As pressure drops are flow dependent, it remains difficult to assess the true significance of a stenosis for low-flow low-gradient patients. Recent advances in segmentation techniques enable patient-specific Computational Fluid Dynamics (CFD) simulations of flow through the aortic valve. In this work a simulation framework is presented and used to analyze data of 18 patients. The ventricle and valve are reconstructed from 4D Computed Tomography imaging data. Ventricular motion is extracted from the medical images and used to model ventricular contraction and corresponding blood flow through the valve. Simplifications of the framework are assessed by introducing two simplified CFD models: a truncated time-dependent and a steady-state model. Model simplifications are justified for cases where the simulated pressure drop is above 10 mmHg. Furthermore, we propose a valve resistance index to quantify stenosis severity from simulation results. This index is compared to established metrics for clinical decision making, i.e. blood velocity and valve area. It is found that velocity measurements alone do not adequately reflect stenosis severity. This work demonstrates that combining 4D imaging data and CFD has the potential to provide a physiologically relevant diagnostic metric to quantify aortic valve stenosis severity.