Arterial Growth and Remodeling in Layered and Toroidal Geometries Using Constrained Mixture Theory

The arterial wall undergoes continual growth and remodeling (G&R) in response to mechanical and biological cues via processes that can also contribute to pathological changes such as aneurysmal dilatation. Computational modeling has proven instrumental in investigating such phenomena. Here, we present a computational framework for arterial G&R based on a constrained mixture theory and applied to simulate aneurysm mechanics in the ascending or descending thoracic aorta. This framework supports curved, multilayered geometries and incorporates diverse tissue constituents, including elastic fibers, fibrillar collagens, smooth muscle cells, and glycosaminoglycans (GAGs). For the descending aorta, we implemented a bilayer configuration and compared the outcomes to prior models and murine data. This multilayered structure confirmed distinct remodeling patterns between the media and adventitia. Inclusion of GAGs, despite their low abundance in healthy tissue, was found to affect the mechanical responses, highlighting yet again their potential role in vascular biomechanics. For the ascending aorta, a parameter study was conducted to assess the impact of vessel curvature and material properties on the G&R process while introducing elastic fiber dysfunction at various locations. Simulations demonstrated that vessel curvature strongly affects both the spatial distribution and extent of remodeling, particularly when mechanical insults localize along the outer curvature. Together, these initial studies further demonstrate the capability and versatility of our modeling platform for investigating region-specific adaptation or disease progression in complex aortic geometries. This framework may support future mechanistic investigations focused on layered vessel structures and constituent-specific dysfunction, contributing to a deeper understanding of aneurysm development and progression.