Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics.

Autor: Hoeijmakers MJMM; Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands; ANSYS France, 69100 Villeurbanne, France. Electronic address: m.j.m.m.hoeijmakers@tue.nl., Silva Soto DA; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Beech Hill Road, S10 2RX Sheffield, United Kingdom., Waechter-Stehle I; Philips Research Laboratories, Röntgenstrasse 24-26, D-22335 Hamburg, Germany., Kasztelnik M; Academic Computer Centre Cyfronet, AGH, University of Science and Technology, Kraków, Poland., Weese J; Philips Research Laboratories, Röntgenstrasse 24-26, D-22335 Hamburg, Germany., Hose DR; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Beech Hill Road, S10 2RX Sheffield, United Kingdom., de Vosse FNV; Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands.
Jazyk: angličtina
Zdroj: Journal of biomechanics [J Biomech] 2019 Sep 20; Vol. 94, pp. 49-58. Date of Electronic Publication: 2019 Jul 17.
DOI: 10.1016/j.jbiomech.2019.07.010
Abstrakt: 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.
(Copyright © 2019 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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