Computational Analysis of Flow Structures in Turbulent Ventricular Blood Flow Associated With Mitral Valve Intervention.

FEM finite element method left ventricle mitral valve clip patient-specific heart modelling triple decomposition of velocity gradient tensor turbulent blood flow

Journal

Frontiers in physiology
ISSN: 1664-042X
Titre abrégé: Front Physiol
Pays: Switzerland
ID NLM: 101549006

Informations de publication

Date de publication:
2022
Historique:
received: 31 10 2021
accepted: 01 04 2022
entrez: 18 7 2022
pubmed: 19 7 2022
medline: 19 7 2022
Statut: epublish

Résumé

Cardiac disease and clinical intervention may both lead to an increased risk for thrombosis events due to a modified blood flow in the heart, and thereby a change in the mechanical stimuli of blood cells passing through the chambers of the heart. Specifically, the degree of platelet activation is influenced by the level and type of mechanical stresses in the blood flow. In this article we analyze the blood flow in the left ventricle of the heart through a computational model constructed from patient-specific data. The blood flow in the ventricle is modelled by the Navier-Stokes equations, and the flow through the mitral valve by a parameterized model which represents the projected opening of the valve. A finite element method is used to solve the equations, from which a simulation of the velocity and pressure of the blood flow is constructed. The intraventricular blood flow is complex, in particular in diastole when the inflow jet from the atrium breaks down into turbulent flow on a range of scales. A triple decomposition of the velocity gradient tensor is then used to distinguish between rigid body rotational flow, irrotational straining flow, and shear flow. The triple decomposition enables the separation of three fundamentally different flow structures, that each generates a distinct type of mechanical stimulus on the blood cells in the flow. We compare the results in a simulation where a mitral valve clip intervention is modelled, which leads to a significant modification of the intraventricular flow. Further, we perform a sensitivity study of the results with respect to the positioning of the clip. It was found that the shear in the simulation cases treated with clips increased more compared to the untreated case than the rotation and strain did. A decrease in valve opening area of 64% in one of the cases led to a 90% increase in rotation and strain, but a 150% increase in shear. The computational analysis opens up for improvements in models of shear-induced platelet activation, by offering an algorithm to distinguish shear from other modalities in intraventricular blood flow.

Identifiants

pubmed: 35846019
doi: 10.3389/fphys.2022.806534
pii: 806534
pmc: PMC9280136
doi:

Types de publication

Journal Article

Langues

eng

Pagination

806534

Informations de copyright

Copyright © 2022 Kronborg, Svelander, Eriksson-Lidbrink, Lindström, Homs-Pons, Lucor and Hoffman.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Joel Kronborg (J)

Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Frida Svelander (F)

Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Samuel Eriksson-Lidbrink (S)

Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Ludvig Lindström (L)

Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Carme Homs-Pons (C)

Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Didier Lucor (D)

Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CNRS, Université Paris-Saclay, Orsay, France.

Johan Hoffman (J)

Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Classifications MeSH