Patient-specific finite element modeling of image-derived shell morphoelastic growth: A general framework and application to stented aortic dissection

Vascular pathology often manifests in complex shape evolution visualized by longitudinal computed tomography (CT) imaging. Growth and remodeling (G&R) frameworks have been integrated into finite element (FE) modeling to probe the mechanics underlying this progression, extending into patient-specific approaches. Their scalability and translatability, however, are limited by the computational burden associated with heterogeneous, anisotropic growth fields and through-thickness meshing. While existing shell growth formulations can mitigate this burden, they require nontrivial FE implementation especially in commercial softwares (e.g., Abaqus) and their efficacy in patient-specific geometries under complex loading is unclear. In this work, we present a general computational framework to implement shell morphoelastic growth computed directly from registered patient-specific CT images. This shell implementation is validated against solid 3D formulations, allows thickness tunability, and increases compatibility with CT surfaces. We demonstrate the robustness of this formulation through modeling post-operative growth in a patient-specific aortic dissection under stent-based loading. Results first show that complex stent-based loading can deform the shell dissection flap to reproduce post-operative geometry. The outer aortic wall is then given a growth field calculated from registration of 1-month and 5-year post-operative CT images, which is implemented along with stent-based loading and pressurization. Results from this model capture the trends in total and lumen morphological evolution observed at intermediate timepoints. These findings establish a general framework, validated with a patient-specific case study of aortic dissection, with the potential to expand to other complex systems, scale to larger patient populations, and apply G&R to clinical translation.