Recent studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) and expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that aneurysm morphology and wall thickness are more predictive of rupture risk and can be the deciding factors in the clinical management of the disease. A non-invasive, imagebased evaluation of AAA shape was implemented on a retrospective study of 10 ruptured and 66 unruptured aneurysms. Three-dimensional models were generated from segmented, contrastenhanced computed tomography images. Geometric indices and regional variations in wall thickness were estimated based on novel segmentation algorithms. A model was created using a J48 decision tree algorithm and its performance was assessed using ten-fold cross validation. Feature selection was performed using the χ 2 -test. The model correctly classified 65 datasets and had an average prediction accuracy of 86.6% (κ = 0.37). The highest ranked features were sac length, sac height, volume, surface area, maximum diameter, bulge height, and intra-luminal thrombus volume. Given that individual AAAs have complex shapes with local changes in surface curvature and wall thickness, the assessment of AAA rupture risk should be based on the accurate quantification of aneurysmal sac shape and size.

The clinical assessment of abdominal aortic aneurysm (AAA) rupture risk is based on the quantification of AAA size by measuring its maximum diameter from computed tomography (CT) images and estimating the expansion rate of the aneurysm sac over time. Recent findings have shown that geometrical shape and size, as well as local wall thickness may be related to this risk; thus, reliable noninvasive image-based methods to evaluate AAA geometry have a potential to become valuable clinical tools. Utilizing existing CT data, the three-dimensional geometry of nine unruptured human AAAs was reconstructed and characterized quantitatively. We propose and evaluate a series of 1D size, 2D shape, 3D size, 3D shape, and second-order curvature-based indices to quantify AAA geometry, as well as the geometry of a size-matched idealized fusiform aneurysm and a patient-specific normal abdominal aorta used as controls. The wall thickness estimation algorithm, validated in our previous work, is tested against discrete point measurements taken from a cadaver tissue model, yielding an average relative difference in AAA wall thickness of 7.8%. It is unlikely that any one of the proposed geometrical indices alone would be a reliable index of rupture risk or a threshold for elective repair. Rather, the complete geometry and a positive correlation of a set of indices should be considered to assess the potential for rupture. With this quantitative parameter assessment, future research can be directed toward statistical analyses correlating the numerical values of these parameters with the risk of aneurysm rupture or intervention (surgical or endovascular). While this work does not provide direct insight into the possible clinical use of the geometric parameters, we believe it provides the foundation necessary for future efforts in that direction.

While further refinement is needed to fully automate the outer wall segmentation algorithm, these preliminary results demonstrate the method's adequate reproducibility and low interobserver variability.

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Neither maximum diameter nor volume measurements over time are able to measure the fastest diameter growth of the aneurysm sac. Consequently, expansion-related wall weakening might be inappropriately reflected by this type of surveillance data. In contrast, localized spots of fast diameter growth can be detected through multiple centerline-based diameter measurements over the entire aneurysm sac. This information might further reinforce the quality of aneurysm surveillance programs.

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Wall stress is a powerful tool to assist clinical decisions in rupture risk assessment of abdominal aortic aneurysms. Key modeling assumptions that influence wall stress magnitude and distribution are the inclusion or exclusion of the intraluminal thrombus in the model and the assumption of a uniform wall thickness. We employed a combined numerical-experimental approach to test the hypothesis that abdominal aortic aneurysm (AAA) wall tissues with different thickness as well as wall tissues covered by different thrombus thickness, exhibit differences in the mechanical behavior. Ultimate tissue strength was measured from in vitro tensile testing of AAA specimens and material properties of the wall were estimated by fitting the results of the tensile tests to a histo-mechanical constitutive model. Results showed a decrease in tissue strength and collagen stiffness with increasing wall thickness, supporting the hypothesis of wall thickening being mediated by accumulation of non load-bearing components. Additionally, an increase in thrombus deposition resulted in a reduction of elastin content, collagen stiffness and tissue strength. Local wall thickness and thrombus coverage may be used as surrogate measures of local mechanical properties of the tissue, and therefore, are possible candidates to improve the specificity of AAA wall stress and rupture risk evaluations.

A better understanding of the inherent properties of vascular tissue to adapt to its mechanical environment is crucial to improve the predictability of biomechanical simulations. Fibrillar collagen in the vascular wall plays a central role in tissue adaptation owing to its relatively short lifetime. Pathological alterations of collagen turnover may fail to result in homeostasis and could be responsible for abdominal aortic aneurysm (AAA) growth at later stages of the disease. For this reason our previously reported multiscale constitutive framework (Martufi, G. & Gasser, T. C. 2011 J. Biomech . 44 , 2544–2550 ( doi:10.1016/j.jbiomech.2011.07.015 )) has been enriched by a collagen turnover model. Specifically, the framework's collagen fibril level allowed a sound integration of vascular wall biology, and the impact of collagen turnover on the macroscopic properties of AAAs was studied. To this end, model parameters were taken from the literature and/or estimated from clinical follow-up data of AAAs (on average 50.7 mm-large). Likewise, the in vivo stretch of the AAA wall was set, such that 10 per cent of collagen fibres were engaged. Results showed that the stretch spectrum, at which collagen fibrils are deposed, is the most influential parameter, i.e. it determines whether the vascular geometry grows, shrinks or remains stable over time. Most importantly, collagen turnover also had a remarkable impact on the macroscopic stress field. It avoided high stress gradients across the vessel wall, thus predicted a physiologically reasonable stress field. Although the constitutive model could be successfully calibrated to match the growth of small AAAs, a rigorous validation against experimental data is crucial to further explore the model's descriptive and predictive capabilities.