A Finite Element Modeling Study of Abdominal Aortic Aneurysm Biomechanics Based on the Relative Effects of Wall and Intraluminal Thrombus Constitutive Material Properties
An abdominal aortic aneurysm (AAA) is a dilation localized in the infrarenal segment of the abdominal aorta that can expand continuously if left untreated. The rupture of an AAA is a nearly fatal event, which carries an overall mortality rate greater than 80%. Clinically, when the diameter of the aorta is greater than 5.0 cm, it is considered to be at high risk of rupture. However, this size-based criterion may not apply for all patients, which has led to biomechanical studies in which the AAA is considered to fail when the wall stresses exceed the ultimate wall strength. Biomechanical parameters such as peak wall stress (PWS), 99th percentile wall stress (99thWS), and spatially average wall stress (SAWS) have been proposed as indicators of rupture risk. Finite Element Analysis (FEA) is a computational method utilized to calculate these parameters in silico based in part on choosing appropriate constitutive material properties for the AAA wall and the intraluminal thrombus (ILT). This study aims to investigate the effect of different constitutive material properties for the wall and ILT on 21 idealized and 10 non-ruptured patient-specific AAA geometries. Three materials were used to characterize the wall and two for the ILT. In total, six material model combinations were used for each AAA geometry subject to appropriate boundary conditions. The results of 181 FEA simulations indicate significant differences in the average PWS, 99thWS, and SAWS for all AAA geometries subject to the choice of a material model combination. Specifically, using a material model combination with a compliant ILT yielded statistically higher wall stresses compared to using a stiff ILT, irrespective of the constitutive equation used to model the AAA wall. This work provides quantitative insight into the uncertainty of the wall stress distributions ensuing from AAA FEA modeling due to its strong dependency on population-averaged soft tissue material characterizations.
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