Complications following endovascular aneurysm repair (EVAR), such as Type IA endoleaks (TIAEL) and limb graft occlusions (LGO), remain a clinical concern. However, existing research aimed at resolving these complications is limited, mainly due to difficulties in visualizing and quantifying the deformation of stent fabric in standard postoperative images. This thesis presents a novel in vitro framework to visualize and quantify stent graft fabric folding in abdominal aortic aneurysm (AAA) phantoms. Silicone phantoms with representative AAA
anatomy were fabricated and validated using a combination of compression tests and a clinical evaluation of elasticity by experienced clinicians. Each phantom was implanted with an
Endurant II stent grafts under varying oversizing conditions. Micro-CT imaging was used to visualize the endograft fabric within the phantoms. A custom convolutional neural network-
based segmentation pipeline was developed to quantify fold severity as the proportion between the total phantom area and the stented lumen. Preliminary comparison with finite element simulations (PrediSurge) demonstrated the feasibility of the method. Therefore, this thesis establishes a validated methodology for fabric fold visualization and quantification, supporting future studies on the relationship between stent graft folding and post-EVAR complications.