A Geometric and Morphoelastic Study of Aortic Dissection Evolution

项目摘要/摘要： 众所周知，在目前的评估和评估方法下，主动脉夹层的自然演变是不可预测的。 管理层。迫切需要更全面地阐明B型主动脉的生物力学稳定性 夹层和识别成像数据中的签名，从而实现基于主动脉的最佳患者分类 脆弱。长期目标是开发和验证基于图像的分析算法来分类 主动脉稳定性和允许个性化的风险分层为给定患者的主动脉几何形状提供基础 以优化临床管理。这项提议的总体目标是利用现代方法 微分几何、连续介质力学和计算机视觉，以发现和表征高风险 隐藏在脆弱动脉的CT血管成像(CTA)数据中的几何结构。中环 这一应用的假设是，在主动脉形状和主动脉稳定性之间存在基本联系，因为 它与主动脉夹层和脆弱的风险有关。这项工作的基本原理是开发一种简单的 基于可平移几何和力学的算法预测解剖稳定性和介入时机 发现隐藏在现有患者成像数据中的更丰富、更细微的主动脉形状映射。这个 中心假设将通过追求三个具体目标来检验：1)发展现代几何分类 对于主动脉形状，2)开发一个计算模型，提供支持该形状的机制 主动脉夹层的演变，以及3)发展一种现代的继承者，以取代传统的“最大直径”。 综合几何、有限元和生理因素的主动脉夹层测量。利用大型 疾病和干预不同阶段的正常和夹层主动脉的预先识别的CTA数据集，目标1 将使用计算机视觉工具将主动脉形状简化为形状指数和曲线度的分布。目标2 将利用先进的形态弹性有限元生长模型来发现生物力学机制 支持主动脉夹层中的主动脉形状变化，并根据患者特定的几何形状验证这些模型 在临床相关的时间段内。这些新的形状和机械稳定性分级器将用于这两个 线性和非线性降维方法确定不同临床条件下的主动脉形状子空间 目标3中的设想。这一提议具有创新性，因为它挑战了评价和治疗现状 采用新的措施和技术来分析临床相关的主动脉几何结构和血管的演变 大动脉的形状。每个病人都被带到手术室，完全是为了有一个积极的临床 结果。这项研究具有重要意义，因为它有望为外科医生和患者提供更多 用于更好地做出关于B型主动脉的知情管理决策的判别性框架 剖析并最终优化结果。

Project Summary/Abstract: The natural evolution of aortic dissection is notoriously unpredictable under current methods of evaluation and management. There is an urgent need to more completely elucidate the biomechanical stability of type B aortic dissections and identify signatures in the imaging data allowing for optimal patient classification based on aortic fragility. The long-term goal is the development and validation of image-based analysis algorithms to classify aortic stability and allow a personalized risk stratification for a given patient’s aortic geometry providing the basis for optimizing clinical management. The overall objective of this proposal is to utilize modern approaches in differential geometry, continuum mechanics, and computer vision to discover and characterize high-risk geometric structures hidden within computed tomography angiography (CTA) data of fragile aortas. The central hypothesis of this application is the existence of a fundamental link between aortic shape and aortic stability as it relates to the risk of aortic dissection and fragility. The rationale for this work is development of an easily translatable geometry and mechanics-based algorithm to predict dissection stability and intervention timing by discovering a richer and more nuanced mapping of aortic shapes hidden in existing patient imaging data. The central hypothesis will be tested by pursuing three specific aims: 1) develop a modern geometric classification for aortic shapes, 2) develop a computational model that provides the mechanism underpinning the shape evolution of aortic dissections, and 3) develop a modern successor to the traditional ‘maximum diameter’ measure of aortic dissections that integrates geometric, finite element, and physiologic factors. Utilizing a large pre-identified CTA data set of normal and dissected aortas at various stages of disease and intervention, aim 1 will use tools from computer vision to reduce aortic shape to distributions of shape index and curvedness. Aim 2 will utilize advanced morphoelastic finite element growth models to discover the biomechanical mechanism underpinning aortic shape changes in aortic dissections and validate these models on patient specific geometries over clinically relevant time periods. These novel shape and mechanical stability classifiers will be used in both linear and non-linear dimensionality reduction methods to define aortic shape sub-spaces for different clinical scenarios in aim 3. This proposal is innovative as it challenges the status quo of evaluation and treatment by deploying novel measures and techniques that analyze clinically relevant aortic geometry and the evolution of aortic shape. Every patient is taken to the operating room under the full intent of having a positive clinical outcome. The research outlined is significant because it is expected to provide surgeons and patients a more discriminative framework with which to make better informed management decisions concerning type B aortic dissections and ultimately optimize outcomes.