On the role of tissue mechanics in fluid–structure interaction simulations of patient‐specific aortic dissection

Author:

Schussnig Richard1ORCID,Rolf‐Pissarczyk Malte2ORCID,Bäumler Kathrin3ORCID,Fries Thomas‐Peter4ORCID,Holzapfel Gerhard A.25ORCID,Kronbichler Martin6ORCID

Affiliation:

1. High‐Performance Scientific Computing University of Augsburg Bavaria Germany

2. Institute of Biomechanics Graz University of Technology Styria Austria

3. Department of Radiology Stanford University Stanford California USA

4. Institute of Structural Analysis Graz University of Technology Styria Austria

5. Department of Structural Engineering Norwegian University of Science and Technology Trøndelag Norway

6. Applied Numerics Ruhr University Bochum North Rhine‐Westphalia Germany

Abstract

AbstractModeling an aortic dissection represents a particular challenge from a numerical perspective, especially when it comes to the interaction between solid (aortic wall) and liquid (blood flow). The complexity of patient‐specific simulations requires a variety of parameters, modeling assumptions and simplifications that currently hinder their routine use in clinical settings. We present a numerical framework that captures, among other things, the layer‐specific anisotropic properties of the aortic wall, the non‐Newtonian behavior of blood, patient‐specific geometry, and patient‐specific flow conditions. We compare hemodynamic indicators and stress measurements in simulations with increasingly complex material models for the vessel tissue ranging from rigid walls to anisotropic hyperelastic materials. We find that for the present geometry and boundary conditions, rigid wall simulations produce different results than fluid–structure interaction simulations. Considering anisotropic fiber contributions in the tissue model, stress measurements in the aortic wall differ, but shear stress‐based biomarkers are less affected. In summary, the increasing complexity of the tissue model enables capturing more details. However, an extensive parameter set is also required. Since the simulation results depend on these modeling choices, variations can lead to different recommendations in clinical applications.

Funder

TU Graz, Internationale Beziehungen und Mobilitätsprogramme

Publisher

Wiley

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