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Objective: We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way. Methods: We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time. Results: A dataset of 7 subjects (4–7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach. Conclusion: We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion. Significance: The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs. [ABSTRACT FROM AUTHOR]

Understanding the mechanical behavior of the arterial wall and its spatial variations is essential for the study of vascular physiopathology and the design of biomedical devices that interact with the arterial wall. Although it is generally accepted that the aortic wall gets stiffer along its length, the spatial variations in the mechanical behavior of the thoracic aorta are not well understood. In this study, therefore, we investigate both longitudinal and circumferential variations in the mechanical properties of the porcine descending thoracic aorta. Using a previously developed experimental method and stress-strain analysis, the stress, stretch, tangent modulus (TM), and pressure-strain elastic modulus (PSEM) are estimated in the range of in vivo pressure. The results show that the longitudinal differences of both TM and PSEM are statistically significant in the posterior region but not in the anterior region. Both moduli are greater in the posterior distal region when compared with the other test regions. The findings of this study meet a need for clarifying the region investigated, especially in circumferential region, to study the regional variations in biomechanics of blood vessels. © 2012 Wiley Periodicals, Inc. J Biomed Mater Res Part A, 2013. [ABSTRACT FROM AUTHOR]

Objectives: Abdominal aortic aneurysms (AAA) that rupture have a high mortality rate. Rupture occurs when local mechanical stress exceeds the local mechanical strength of an AAA, so stress profiles such as those from finite element analysis (FEA) are useful. The role and effect of surrounding tissues, like the vertebral column, which have not been extensively studied, are examined in this paper., Methods: Longitudinal CT scans from ten patients with AAAs were studied to see the effect of surrounding tissues on AAAs. Segmentation was performed to distinguish the AAA from other tissues and we studied how these surrounding tissues affected the shape and curvature of the AAA. Previously established methods by Veldenz et al. were used to split the AAA into 8 sections and examine the specific effects of surrounding tissues on these sections [1]. Three-dimensional models were created to better examine these effects over time. Registration was done in order to compare AAAs longitudinally., Results: The vertebral column and osteophytes were observed to have been affecting the shape and the curvature of the AAA. Interaction with the spine caused focal flattening in certain areas of the AAA. In 16 of the 41 CT scans, the right posterior dorsal section (section 5), had the highest radius of curvature, which was by far the section that had the maximum radius for a specified CT scan. Evolution of the growing AAA showed increased flattening in this section when comparing the last CT scan to the first scan., Conclusion: Surrounding tissues have a clear influence on the geometry of an AAA, which may in turn affect the stress profile of AAA. Incorporating these structures in FEA and G&R models will provide a better estimate of stress., Clinical Relevance: Currently, size is the only variable considered when deciding whether to undergo elective surgery to repair AAA since it is an easy enough measure for clinicians to utilize. However, this may not be the best indicator of rupture risk because small aneurysms also contribute to a high mortality rate. AAA's wall stress is a superior indicator and may be better predicted with the inclusion of these surrounding tissues, which then could be used by clinicians in their decision-making process on whether to operate on an AAA.

It is now well known that altered hemodynamics can alter the genes that are expressed by diverse vascular cells, which in turn plays a critical role in the ability of a blood vessel to adapt to new biomechanical conditions and governs the natural history of the progression of many types of disease. Fortunately, when taken together, recent advances in molecular and cell biology, in vivo medical imaging, biomechanics, computational mechanics, and computing power provide an unprecedented opportunity to begin to understand such hemodynamic effects on vascular biology, physiology, and pathophysiology. Moreover, with increased understanding will come the promise of improved designs for medical devices and clinical interventions. The goal of this paper, therefore, is to present a new computational framework that brings together recent advances in computational biosolid and biofluid mechanics that can exploit new information on the biology of vascular growth and remodeling as well as in vivo patient-specific medical imaging so as to enable realistic simulations of vascular adaptations, disease progression, and clinical intervention.