Bayesian Inference-Based Estimation of Normal Aortic, Aneurysmal and Atherosclerotic Tissue Mechanical Properties: From Material Testing, Modeling and Histology

Abstract

Objective: Mechanical properties of healthy, aneurysmal, and atherosclerotic arterial tissues are essential for assessing the risk of lesion development and rupture. Strain energy density function (SEDF) has been widely used to describe these properties, where material constants of the SEDF are traditionally determined using the ordinary least square (OLS) method. However, the material constants derived using OLS are usually dependent on initial guesses. Methods: To avoid such dependencies, Bayesian inference-based estimation was used to fit experimental stress-stretch curves of 312 tissue strips from 8 normal aortas, 19 aortic aneurysms, and 21 carotid atherosclerotic plaques to determine the constants, C 1 , D 1 , and D 2 of the modified Mooney-Rivlin SEDF. Results: Compared with OLS, material constants varied much less with prior in the Bayesian inferencebased estimation. Moreover, fitted material constants differed amongst distinct tissue types. Atherosclerotic tissues associated with the biggest D 2 , an indicator of the rate of increase in stress during stretching, followed by aneurysmal tissues and those from normal aortas. Histological analyses showed that C 1 and D 2 were associated with elastin content and details of the colla

Coronary artery properties in atherosclerosis: A deep learning predictive model

“…The estimation of the mechanical properties may provide rich information to identify plaque vulnerability, such as whether an atherosclerotic coronary artery suffers from a reduced collagen synthesis, local overexpression of collagenase, or smooth muscle cell apoptosis ( Libby, 2001 ; Akyildiz et al., 2011 ; Davis et al., 2016 ; Wang et al., 2019 ; O’Reilly et al., 2020 ). This information can also be used to evaluate the impact of different drugs in treating atherosclerosis.…”

“…However, an approach performed with adult mini-pigs prone to atherosclerosis showed that necrotic core sizes determined by virtual and real histology were different, thus questioning the capacity of virtual histology to detect prone plaques ( Thim, 2010 ). Moreover, none of these techniques have the capacity to quantify the mechanical properties of the arterial wall, which is essential to estimate stress distribution ( Akyildiz et al., 2011 ; Wang et al., 2019 ). One promising approach is the iMOD elastography ( Le Floc’h et al., 2010 ), which developed several computational algorithms to rebuild elasticity strain maps inside the wall based on the prediction of the stress maps using IVUS-derived techniques.…”