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 concer...