On the role of material properties in ascending thoracic aortic aneurysms

Abstract

One of the obstacles standing before the biomechanical analysis of an ascending thoracic aortic aneurysm (ATAA) is the difficulty in obtaining patient-specific material properties. This study aimed to evaluate differences on ATAA-related stress predictions resulting from the elastostatic analysis based on the optimization of arbitrary material properties versus the application of patient-specific material properties determined from ex-vivo biaxial testing. Specifically, the elastostatic analysis relies the on the fact that, if the aortic wall stress does not depend on material properties, the aorta has to be statistically determinate. Finite element analysis (FEA) was applied to a group of nine patients who underwent both angio-CT imaging to reconstruct ATAA anatomies and surgical repair of diseased aorta to collect tissue samples for experimental material testing. Tissue samples cut from excised ATAA rings were tested under equibiaxial loading conditions to obtain experimentally-derived material parameters by fitting stress-strain profiles. FEAs were carried out using both optimized and experimentally-derived material parameters to predict and compare the stress distribution using the mean absolute percentage error (MAPE). Although physiological strains were below yield point (range of 0.08–0.25), elastostatic analysis led to errors on the stress predictions that depended on the type of constitutive model (highest MAPE of 0.7545 for Yeoh model and 0.7683 for Fung model) and ATAA geometry (lowest MAPE of 0.0349 for patient P.7). Elastostatic analysis needs better understanding of its application for determining aneurysm mechanics, and patient-specific material parameters are essential for reliable accurate stress predictions in ATAAs.

Introduction

A ruptured ascending thoracic aortic aneurysm (ATAA) is considered a surgical emergency since progressive dilatation is often fatal if this disease is not detected by diagnostic imaging and managed immediately [1]. Despite being a relatively rare event with an estimated incidence of 5.0 per 100,000 individuals per year, the risk of fatal complications such as rupture or acute dissections can be as high as 50% in patients with a large ATAA wall (aortic diameter >50 mm) [2,3]. The risk over time of ATAA development to a size of 40–45 mm in patients with a congenital bicuspid aortic valve (BAV) versus the morphological normal tricuspid aortic valve (TAV) is remarkable. Several studies highlighted that 84% of individuals with BAV may develop aortopathy during the life course [4,5]. With regards to ATAA, degenerative aneurysms tend to develop in the mid-ascending aorta and then progress distally and proximally while ATAAs associated with connective tissue disorders are usually confined to the aortic root [6].

Although the aortic size criterion can be adjusted to achieve higher patient specificity using the body surface area or patient height [7], the surgical dilemma still exists because fatal complications can occur at aortic diameters lower than that dictated by current clinical guidelines for elective repair of aneurysmal aorta [8]. There is a need to delineate additional metrics, not based on aortic size, to better identify the risk of ATAA failure. Biomechanical risk assessments using finite element analysis (FEA) to estimate the wall stress exerted on the diseased aorta have been proposed in abdominal aortic aneurysms [9,10] and ATAAs [11,12]. These approaches for risk stratification appeared to be promising since peak wall stress can be calculated from routinely performed CT scans and may be a better predictor of risk of rupture than aortic diameter [13]. Recently, FEA was combined with machine learning techniques to study the relationship between shape features and wall stress as risk metric of ATAA, towards the development of computer-aided-diagnosis [14].

FEAs depend on several factors including the aortic geometry, the loading condition induced by hemodynamic and structural loads and the material properties of aortic wall constituents. Hemodynamic can be evaluated by computational fluid dynamic [[15], [16], [17], [18]] or in-vivo 4D flow MRI [19] while tracking algorithms of aortic wall surface detected by dynamic CT [20] or MRI [21] can be adopted to estimate the ATAA-related structural mechanics. Obtaining material parameters non-invasively during patient monitoring for preoperative risk estimations represent an important challenge. However, if the stress distribution does not depend on material properties, the structure has to be statically determinate [22,28]. Under this condition, we can eliminate the need for patient-specific material properties and the FEA can be performed with arbitrary material properties because they do not affect the resulting wall stress. Several research groups adopted this approach to compute wall stress of abdominal [22] and ascending aortic aneurysms [[23], [24], [25], [26]].

In this proof-of-concept, we want to know how different would be the resulting stress distribution on the aneurysm wall if material properties derived by an elastostatic analysis proposed by Liu et al. [23] are used as compared to FEAs using patient-specific material properties determined from ex-vivo biaxial testing. If large differences of stress distributions are observed, one could raise a red flag for further investigation using this appealing approach. To accomplish this task, we carried out FEAs on nine patients who underwent both dynamic CT imaging and surgical elective repair of ATAA to both reconstruct aortic geometries for FEA and collect tissue samples for patient-specific material property evaluation by the fitting of experimental stress-strain curves. Both an isotropic- (ie, two-term Yeoh model) and an anisotropic (ie, Fung-exponential model) constitutive formulation were tested. A stress comparison using the optimal material set versus the experimentally-derived material set was performed, and results were discussed.