Morphoelastic Shell Growth Algorithm to Enable Efficient Modeling of Complex Evolutions in Patient-Specific Aortic Geometry

A hallmark of the progression of vascular pathology is complex shape evolution visualized by computed tomography (CT) images at different time points. Simple geometric measurements (e.g. maximum diameter) from CT imaging are used to quantify disease progression, yet a mechanistic understanding and an ability to predict the shape changes of the diseased aorta are lacking. Growth and remodeling algorithms to capture shape evolution have been trialed, however these have been limited by computational burden. This work presents an efficient finite element (FE) framework that can study the patient-specific shape changes in aortic pathologies. First, the implementation of morphoelastic processes using shell elements in FE is presented with comparison against the 3D element implementations and benchmark cases available in the literature. Next, we incorporate localized surface area changes extracted from CT images in patient-specific aortas, as implemented with 3D elements in our recently published work, to study the effectiveness of a shell FE growth framework for shape-driven growth. Lastly, we demonstrate the utility of our shell element FE framework through in silico stent deployment. Comparing to serial clinical imaging, this methodology couples with geometric analysis of aortic pathology to capture growth and shape evolutions, thus opening avenues to improve FE modeling of aortic degeneration and identify hitherto unknown biomechanical signals of surgical procedure failure.