DIC for Surface Motion Analysis Applied to Displacement of a Stent Graft for Abdominal Aortic Repair in a Pulsating Flow

Stent graft migration has been recognized to influence the long-term durability of endovascular aortic repair. Flow-induced displacement forces acting on the attachment zones may contribute to this migration. An experimental perfusion model consisting of the flow loop described by Roos et al. 2014 was used for further characterization of the pulsating flow induced stent graft movements with monocular and stereoscopic configurations of an optical imaging system. This paper adds new information on displacement measurement accuracy and 3D deformation analysis of the stent graft, which is used for abdominal aortic aneurysm treatment. The work describes used modification of Soloff’s Stereo PIV reconstruction algorithm for surface motion analysis. It was found that the oscillation of the stent graft’s body in the perpendicular direction to the front plane was 5 times less than side movements of the bent stent graft. These results can be used for further studies on different stent graft geometrical configurations and CFD simulations using fluid-structure interaction approach

Virtuelle endovaskuläre Versorgung von abdominalen Aortenaneurysmen

This thesis is focused on computational methods that predict the outcome of endovascular repair of abdominal aortic aneurysms. Novelties include improvements of the aneurysm model, the stent-graft model as well as the in-silico stent-graft placement methodology. The newly developed methods are applied to patient-specific cases and are validated against real-world postinterventional data. Further, directions for using the in-silico model of endovascular aneurysm repair as personalized preinterventional planning tool in clinical practice are provided.Die vorliegende Arbeit beschäftigt sich mit numerischen Methoden um den Ausgang einer endovaskulären Versorgung von abdominalen Aortenaneurysmen vorherzusagen. Neuheiten umfassen Verbesserungen des Aneurysmenmodells, des Stentgraftmodells sowie der virtuellen Platzierungsmethode des Stentgrafts. Die neu entwickelten Methoden werden auf patientenspezifische Fälle angewandt und werden mit realen postoperativen Daten validiert. Weiterhin werden klinische Anwendungen des Modells der endovaskulären Aneurysmenversorgung als personalisiertes präoperatives Planungswerkzeug präsentiert

Design of a comprehensive modeling, characterization, rupture risk assessment and visualization pipeline for Abdominal Aortic Aneurysms

Abdominal aortic aneurysms (AAA) is a dilation of the abdominal aorta, typically within the infra-renal segment of the vessel that cause an expansion of at least 1.5 times the normal vessel diameter. It is becoming a leading cause of death in the United States and around the world, and consequentially, in 2009, the Society for Vascular Surgery (SVS) practice guidelines expressed the critical need to further investigate the factors associated with the risk of AAA rupture, along with potential treatment methods. For decades, the maximum diameter (Dmax) was introduced as the main parameter used to assess AAA behavior and its rupture risk. However, it has been shown that three main categories of parameters including geometrical indices, such as the maximum transverse diameter, biomechanical parameters, such as material properties, and historical clinical parameters, such as age, gender, hereditary history and life-style affect AAA and its rupture risk. Therefore, despite all efforts that have been undertaken to study the relationship among different parameters affecting AAA and its rupture, there are still limitations that require further investigation and modeling; the challenges associated with the traditional, clinical quality images represent one class of these limitations. The other limitation is the use of the homogenous hyper-elastic material property model to study the entire AAA, when, in fact, there is evidence that different degrees of degradation of the elastin and collagen network of the AAA wall lead to different regions of the AAA exhibiting different material properties, which, in turn, affect its biomechanical behavior and rupture. Moreover, the effects of all three main categories of parameters need to be considered simultaneously and collectively when studying the AAAs and their rupture, so once again, the field can further benefit from such studies. Therefore, in this work, we describe a comprehensive pipeline consisting of three main components to overcome some of these existing limitations. The first component of the proposed method focuses on the reconstruction and analysis of both synthetic and human subject-specific 3D models of AAA, accompanied by a full geometric parameter analysis and their effects on wall stress and peak wall stress. The second component investigates the effect of various biomechanical parameters, specifically the use of various homogeneous and heterogeneous material properties to model the behavior of the AAA wall. To this extent, we introduce two different patient-specific regional material property models to better mimic the physiological behavior of the AAA wall. Finally, the third component utilizes machine learning methods to develop a comprehensive predictive model that incorporates the effect of the geometrical, biomechanical and historical clinical data to predict the rupture severity of AAA in a patient-specific manner. This is the first comprehensive semi-automated method developed for the assessment of AAA. Our findings illustrate that using a regional material property model that mimics the realistic heterogeneity of the vessel’s wall leads to more reliable and accurate predictions of AAA severity and associated rupture risk. Additionally, our results indicate that using only Dmax as an indicator for the rupture risk is insufficient, while a combination of parameters from different sources along with PWS could serve as a more reliable rupture assessment. These methods can help better characterize the severity of AAAs, better predict their associated rupture risk, and, in turn, help clinicians with earlier, patient-customized diagnosis and patient-customized treatment planning approaches, such as stent grafting

Motion Calculations on Stent Grafts in AAA

Endovascular aortic repair (EVAR) is a technique which uses stent grafts to treat aortic aneurysms in patients at risk of aneurysm rupture. Although this technique has been shown to be very successful on the short term, the long term results are less optimistic due to failure of the stent graft. The pulsating blood flow applies stresses and forces to the stent graft, which can cause problems such as breakage, leakage, and migration. Therefore it is of importance to gain more insight into the in vivo motion behavior of these devices. If we know more about the motion patterns in well-behaved stent graft as well as ill-behaving devices, we shall be better able to distinguish between these type of behaviors These insights will enable us to detect stent-related problems and might even be used to predict problems beforehand. Further, these insights will help in designing the next generation stent grafts. Firstly, this work discusses the applicability of ECG-gated CT for measuring the motions of stent grafts in AAA. Secondly, multiple methods to segment the stent graft from these data are discussed. Thirdly, this work proposes a method that uses image registration to apply motion to the segmented stent mode