Modelling Growth, Remodelling and Damage of a Thick-walled Fibre-reinforced Artery with Active Response: Application to Cerebral Vasospasm and Treatment

• Main Authors: Pederzani, Giulia, Grytsan, Andrii, Hoekstra, Alfons G, Robertson, Anne M, Watton, Paul N
• Format: Journal Article
• Language: English
• Published: 30.08.2024
• Subjects: Computer Science - Computational Engineering, Finance, and Science
• DOI: 10.48550/arxiv.2408.17206

Cerebral vasospasm, a prolonged constriction of cerebral arteries, is the first cause of morbidity and mortality for patients who survive hospitalisation after aneurysmal subarachnoid haemorrhage. The recent finding that stent-retrievers can successfully treat the disease has challenged the viewpoint that damage to the extracellular matrix is necessary. We apply a 3D finite element rate-based constrained mixture model (rb-CMM) to simulate vasospasm, remodelling and treatment with stents. The artery is modelled as a thick-walled fibre-reinforced constrained mixture subject to physiological pressure and axial stretch. The model accounts for distributions of collagen fibre homeostatic stretches, VSMC active response, remodelling and damage. After simulating vasospasm and subsequent remodelling of the artery to a new homeostatic state, we simulate treatment with commonly available stent-retrievers. We perform a parameter study to examine how arterial diameter and thickness affect the success of stent treatment. The model predictions on the pressure required to mechanically resolve the constriction are consistent with stent-retrievers. In agreement with clinical observations, our model predicts that stent-retrievers tend to be effective in arteries of up to 3mm diameter, but fail in larger ones. Variations in arterial wall thickness significantly affect stent pressure requirements. We have developed a novel rb-CMM that accounts for VSMC active response, remodelling and damage. Consistently with clinical observations, simulations predict that stent-retrievers can mechanically resolve vasospasm. Moreover, accounting for a patient's arterial properties is important for predicting likelihood of stent success. This in silico tool has the potential to support clinical decision-making and guide the development and evaluation of dedicated stents for personalised treatment of vasospasm.