An evaluation of fiber-based damage for assessing the failure of aortic tissue: comparison between healthy and aneurysmal aortas

Abstract

The decision of surgical intervention for an aortic aneurysm is usually associated with an assessment of risk of its rupture. Global rupture risk assessment parameters like wall diameter and growth of the aneurysm over time often fail at predicting the risk of rupture with accuracy. This paper will investigate the hypothesis that the tissue’s microstructure determines its macroscopic failure. To this aim, two different testing protocols have been implemented. Human ascending thoracic aortic aneurysm (ATAA) tissue samples were subjected to bulge-inflation testing until rupture coupled with multi-photon microscopy (MPM) imaging. Image stacks of the sample were acquired at different pressure levels. Additionally, porcine aorta samples were tested under uniaxial tension until failure and their response was recorded. Prior to mechanical testing, MPM image stacks were acquired at four different zones on the sample. The image stacks acquired at the load free state were used to extract morphological information relating to collagen fibers. Then, an inverse random sampling approach was used to generate pseudomorphological parameters for network reconstruction. A discrete model of the collagen network signifying its stochastic nature was then developed, including both prefailure and post-failure mechanics. The model was able to replicate the mechanical response and failure of the tissue, and demonstrated that fiber-based damage can strongly shape the macroscopic failure response of the tissue. Identified values of collagen fiber failure strain were in the range of 8.8 to 29.3% in the case of aneurysmal samples, and 18.7 to 25.5% in the case of porcine samples. A statistical analysis enabled the characterization of correlation between fiber morphology and tissue failure. The model may serve as a useful tool for predicting macroscale failure of the aortic wall based on the variations in microscale morphology.