Risk factors and mouse models of abdominal aortic aneurysm rupture

Abdominal aortic aneurysm (AAA) rupture is an important cause of death in older adults. In clinical practice, the most established predictor of AAA rupture is maximum AAA diameter. Aortic diameter is commonly used to assess AAA severity in mouse models studies. AAA rupture occurs when the stress (force per unit area) on the aneurysm wall exceeds wall strength. Previous research suggests that aortic wall structure and strength, biomechanical forces on the aorta and cellular and proteolytic composition of the AAA wall influence the risk of AAA rupture. Mouse models offer an opportunity to study the association of these factors with AAA rupture in a way not currently possible in patients. Such studies could provide data to support the use of novel surrogate markers of AAA rupture in patients. In this review, the currently available mouse models of AAA and their relevance to the study of AAA rupture are discussed. The review highlights the limitations of mouse models and suggests novel approaches that could be incorporated in future experimental AAA studies to generate clinically relevant results

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces

Biomechanical Investigation of Disturbed Hemodynamics-Induced Tissue Degeneration in Abdominal Aortic Aneurysms Using Computational and Experimental Techniques.

Abdominal aortic aneurysm (AAA) is the dilatation of the aorta beyond 50% of the normal vessel diameter. It is reported that 4-8% of men and 0.5-1% of women above 50 years of age bear an AAA and it accounts for ~15,000 deaths per year in the United States alone. If left untreated, AAA might gradually expand until rupture; the most catastrophic complication of the aneurysmal disease that is accompanied by a striking overall mortality of 80%. The precise mechanisms leading to AAA rupture remains unclear. Therefore, characterization of disturbed hemodynamics within AAAs will help to understand the mechanobiological development of the condition which will contribute to novel therapies for the condition. Due to geometrical complexities, it is challenging to directly quantify disturbed flows for AAAs clinically. Two other approaches for this investigation are computational modeling and experimental flow measurement. In computational modeling, the problem is first defined mathematically, and the solution is approximated with numerical techniques to get characteristics of flow. In experimental flow measurement, once the setup providing physiological flow pattern in a phantom geometry is constructed, velocity measurement system such as particle image velocimetry (PIV) enables characterization of the flow. We witness increasing number of applications of these complimentary approaches for AAA investigations in recent years. In this paper, we outline the details of computational modeling procedures and experimental settings and summarize important findings from recent studies, which will help researchers for AAA investigations and rupture mechanics

Computational estimation of haemodynamics and tissue stresses in abdominal aortic aneurysms

'o e Abdominal aortic aneurysm is a vascular disease involving a focal dilation of the aorta. The exact cause is unknown but possibilities include infection and weakening of the connective tissue. Risk factors include a history of atherosclerosis, current smoking and a close relative with the disease. Although abdominal aortic aneurysm can affect anyone, it is most often seen in older men, and may be present in up to 5.9 % of the population aged 80 years. Biomechanical factors such as tissue stresses and shear stresses have been shown to play a part in aneurysm progression, although the specific mechanisms are still to be determined. The growth rate of the abdominal aortic aneurysm has been found to correlate with the peak stress in the aneurysm wall and the blood flow is thought to influence disease development. In order to resolve the connections between biology and biomechanics, accurate estimations of the forces involved are required. The first part of this thesis assesses the use of computational fluid dynamics for modelling haemodynamics in abdominal aortic aneurysms. Boundary conditions from the literature o

Computational Investigation of the Effect of Wall Thickness on Rupture Risk in Abdominal Aortic Aneurysms

Cardiovascular disorders are among the most important causes of sudden death and adult disability worldwide. Abdominal aortic aneurysm (AAA) is a critical clinical condition where the aorta dilates beyond 50% of its normal diameter and leads to a risk of rupture. In this study, we performed fluid-structure interaction (FSI) analysis on an eccentric computational AAA model in order to investigate the effects of wall thickness on AAA wall stresses, which are critically important to estimate the rupture risk. For this purpose, we modeled the problem domain using finite element analysis, and coupled the solutions of fluid and structure domains for improving the accuracy of results. ANSYS commercial finite element analysis software was used for modeling, solving, and post-processing the results. Expanded diameter in AAA sac resulted in altered hemodynamics. Wall shear stresses (WSS) caused by the flow are quite low on the AAA sac, which may deteriorate the endothelial cell regeneration and vascular remodeling in the long term. It is concluded that the most critical region for the rupture risk is the posterior distal end of AAA sac due to being exposed to peak mechanical stresses during the cardiac cycle. Obtained results shed light in understanding the rupture risk assessment of AAA.Qatar University Research Fund, Qatar Universit