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Abdominal Aortic Aneurysms (AAA) is a form of vascular disease causing focal enlargement of abdominal aorta. It affects a large part of population and has up to 90% mortality rate. Since risks from open surgery or endovascular repair outweighs the risk of AAA rupture, surgical treatments are not recommended with AAA less than 5.5cm in diameter. Recent clinical recommendations suggest that people with small aneurysms should be examined 3∼36 months depending on size to get information about morphological changes. While advances in biomechanics provide state-of-the-art spatial estimates of stress distributions of AAA, there are still limitations in modeling its time evolution. Thus, there is no biomechanical framework to utilize such information from a series of medical images that would aid physicians in detecting small aneurysms with high risk of rupture. For the present study, we use series of CT images of small AAAs taken at different times to model and predict the spatio-temporal evolution of AAA. This is achieved using sparse local Gaussian process regression.

For an abdominal aortic aneurysm (AAA) in vivo there are multiple tissues contacting its boundary, none of which have been fully considered for their effect throughout disease progression. Trends such as arterial asymmetry, surface curvature flattening, and arterial tortuosity could be significantly influenced by both surrounding tissue and hemodynamic factors. In order to quantify either the combined or separate influence of such factors during disease progression a precise characterization of aneurysm geometry evolution is needed. Multiple methods for geometrical parameterization of abdominal aortic aneurysms (AAAs) have been previously developed using isolated patient CT scan data but the focus has been mainly on the association of such geometrical parameters with the rupture risk and the efficacy of the parameterization is not fully investigated for a longitudinal study yet (multiple CT scans per patient at progressive intervals) [1]. For this study we have produced a series of 3D models for AAAs in longitudinal studies, developed an arterial centerline generation algorithm, and automated a geometric parameterization procedure for the arterial surfaces. It should be noted that the caliber of our collection of data is relatively rare as it is high resolution, features many patients, and on average has 4–5 images per patient.

Abdominal Aortic Aneurysm (AAA), a focal enlargement of the abdominal aorta is an ongoing process that can be affected by many parameters. Among these parameters, hemodynamics and intraluminal thrombus layer (ILT) play important roles on AAA growth. It is widely accepted that hemodynamic forces (normal and shear forces) have a profound impact on the mechano-homeostasis of the arterial wall and its vascular remodeling. The role of ILT, however, remains controversial. Some studies suggest that ILT may be beneficial by shieling the weak aneurysm wall, whereas others claim that the presence of ILT can lead to immune responses that increase protease breakdown of collagen and elastin, adversely affecting wall strength. ILT is formed by the deposition of blood clots called thrombus. Thrombus formation is achieved through different mechanisms, but all research agrees that shear fluid forces are one of the key parameters for the formation and development of ILT. There are few studies to date that use these three parameters to assess the evolution of AAAs growth. Here, we explore the relation between wall shear stress (WSS), ILT and AAA expansion using longitudinal CT images from follow-up studies from 3 patients (a total of 8 scans). We used geometrical models of AAAs segmented from patient images to estimate outer surface displacement, ILT, and tissue thickness. Additionally, we used fluid dynamic data to estimate wall shear stress at peak systolic. These parameters were then used to investigate possible relationships with each other.

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