Development of a Collagen Fibre Remodelling Rupture Risk Metric for Potentially Vulnerable Carotid Artery Atherosclerotic Plaques

Abstract

Atherosclerotic plaque rupture in carotid arteries can lead to stroke which is one of the leading causes of death or disability worldwide. The accumulation of atherosclerotic plaque in an artery changes the mechanical properties of the vessel. Whilst healthy arteries can continuously adapt to mechanical loads by remodelling their internal structure, particularly the load-bearing collagen fibres, diseased vessels may have limited remodelling capabilities. In this study, a local stress modulated remodelling algorithm is proposed to explore the mechanical response of arterial tissue to the remodelling of collagen fibres. This stress driven remodelling algorithm is used to predict the optimum distribution of fibres in healthy and diseased human carotid bifurcations obtained using Magnetic Resonance Imaging (MRI). In the models, healthy geometries were segmented into two layers: media and adventitia and diseased into four components: adventitia, media, plaque atheroma and lipid pool (when present in the MRI images). A novel meshing technique for hexahedral meshing of these geometries is also demonstrated. Using the remodelling algorithm, the optimum fibre patterns in various patient specific plaques are identified and the role that deviations from these fibre configurations in plaque vulnerability is shown. This study provides critical insights into the collagen fibre patterns required in carotid artery and plaque tissue to maintain plaque stability.

Inverse material parameter estimation of patient‐specific finite element models at the carotid bifurcation: The impact of excluding the zero‐pressure configuration and residual stress

“…It is expected that if higher levels of axial strain are imposed, the angle of fibers would alter and would start to play a more significant role in the stress distribution and peak stress values. 14 To conclude, this study has shown the importance of including these methods in determining a more accurate stress distribution throughout the vessel wall. Consequently, for stress to be used clinically as a rupture vulnerability measure, these methods must be used to provide a better estimate of the stress being experienced in‐vivo.…”

“…This could be from the fact that different ranges of axial strain were not implemented in our material parameter estimation and kept constant for all models. It is expected that if higher levels of axial strain are imposed, the angle of fibers would alter and would start to play a more significant role in the stress distribution and peak stress values 14 . To conclude, this study has shown the importance of including these methods in determining a more accurate stress distribution throughout the vessel wall.…”

“…New diagnostic metrics that can aid physician treatment decisions need to be developed. Many studies have used patient‐specific finite element (FE) models to calculate stresses and strains for possible rupture predictions in both aneurysms and atherosclerotic plaques 7–16 . These models are often derived from diagnostic images such as CT, 17–21 Ultrasound, 22,23 or MRI 24,25 and provide physiological data that can act as inputs for computational analysis.…”

“…Many studies have used patient‐specific finite element (FE) models to calculate stresses and strains for possible rupture predictions in both aneurysms and atherosclerotic plaques. 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 These models are often derived from diagnostic images such as CT, 17 , 18 , 19 , 20 , 21 Ultrasound, 22 , 23 or MRI 24 , 25 and provide physiological data that can act as inputs for computational analysis. It has been suggested that accurately characterizing the stress and strain in patient‐specific models could lead to a more accurate diagnosis of vessel or plaque rupture vulnerability when compared to standard diagnostic measures.…”

Layer‐specific fiber distribution in arterial tissue modeled as a constrained mixture

“…Other reorientation algorithms have successfully predicted the fiber distribution in various tissues such as mitral valves [2,4], carotid arteries [9,31], articular cartilage [5] and cornea [32]. The most significant difference between these kind of tissues is the mechanical surrounding environment which dictates the fiber distribution.…”