Growth and Remodeling Model of Abdominal Aortic Aneurysm: Toward Clinical Applica

描述（由申请人提供）：在过去的二十年中，对腹主动脉瘤（AAAs）的病理生理学和生物力学的理解取得了重大进展。特别是，在计算生物力学分析中结合患者特定的几何形状，有望使计算生物力学成为AAA风险评估的重要工具。然而，传统的有限元分析仅使用先进的医学成像技术来确定患者特定病变的几何形状，而不将几何特征与扩大过程中长期血管适应所导致的生物力学变化联系起来。我们之前研究了从3例患者的纵向研究中获得的小AAAs的CT图像，并使用了生长和重塑（G&R）模型

DESCRIPTION (provided by applicant): During the past two decades, there have been significant advances in understanding of pathophysiology and biomechanics of abdominal aortic aneurysms (AAAs). In particular, incorporation of patient-specific geometries in computational biomechanical analysis promises that the computational biomechanics becomes an essential tool for AAA risk assessment. Conventional finite element analysis, however, uses advances in medical imaging only to define patient-specific lesion geometry, but do not relate the geometrical features with alterations in biomechanics from long-term vascular adaptation during the enlargement. We previously studied CT images of small AAAs obtained from longitudinal studies of three patients and, using a growth and remodeling (G&R) model of AAAs developed from our group, found that the stress distribution of AAAs can be altered by interacting with spine vertebrae during the enlargement. In collaboration with Dr. Whal Lee at Seoul National University Hospital in South Korea, we have obtained two to nine sets of follow-up CT images from eight more patients with the mean surveillance interval of 355 days. The main goals of this project, therefore, are i) to exploit this unique data ad to understand biomechanically why the observed changes in morphology of AAAs occur, ii) to identify specific morphological features that will estimate the stress distribution better and iii) to develop a computational framework to better predict rupture risk for small AAAs based on medical images and computational analyses over a small number of longitudinal studies. The specific aims are: (1) to develop a quantitative image analysis to characterize morphological changes of AAAs using series of 3D CT images, and investigate correlations between geometrical parameters and the local expansion, (2) to develop a numerical inverse method using a novel G&R model and two longitudinal images from the same patient to predict the evolution of lesion geometry, and (3) to utilize the results from Aim &2 and the G&R model of AAAs to study effects of local wall expansion and interactions with the spinal vertebrae on the lesion geometry, stress distribution, and rupture risk. The uniqueness of this research stems from our novel G&R model and the unique set of longitudinal patient data that is essential for building and validating such models. The proposed research will significantly increase our understanding of biomechanics of AAAs during their progression and will provide a necessary step toward the clinical application of vascular G&R models for AAA risk assessment and treatment.