Abdominal Aortic Aneurysm (AAA) is an irreversible dilation of the lower section of the aorta that poses lethal threat to the patient. Medical intervention has been conducted for decades via open surgery, yet more recently, the minimally invasive technique of endovascular aneurysm repair (EVAR) is preferred, via the introduction of a stent graft into the aneurysmal region. In this thesis, finite element analysis (FEA) techniques have been developed to model the AnacondaTM stent graft. The aim is to produce the necessary numerical tools to allow for personalized EVAR simulations for mechanical and clinical evaluation. The basic unit of an AnacondaTM is modelled with a novel FEA approach acquiring computationally efficient solutions without sacrificing precision. By taking into account the manufacturing process, the developed strains and forces can be predicted, allowing for fatigue life and anchoring evaluation. The mean strain of the unit is found to be strongly affected by the oversize of the device, yet its radial force is mainly influenced by the friction of the vessel/stent interface. The effects of non-circular aortic cross sections are also examined and mean aortic diameter approximations are shown to be superior. For all analyses, a phenomenological model of the abdominal aorta is created, able to accurately mimic the pressure-radius response of the artery. Furthermore, a separate model of lower fidelity and higher computational efficiency is developed, allowing the simulation of the full AnacondaTM stent graft. The model has the ability to predict the deployed position of the device and demonstrates that inclusion of fabric folds can allow for more insightful hemodynamic studies that capture blood recirculation. Finally, an extensive statistical analysis of 258 patient geometries is conducted and a set of 10 angles is proposed as a way to quantify the AAA shape. No anterior/posterior or lateral symmetries are identified. The measurements of angular and dimensional variables correspond to the most thorough study of the AAA shape available in the literature and allow the identification of average and worst case topologies for future EVAR simulations.
|Date of Award||8 Jan 2019|
- University Of Strathclyde
|Sponsors||University of Strathclyde|
|Supervisor||William Dempster (Supervisor) & David Nash (Supervisor)|