From NIH specific aims

Aortic dissection is a devastating clinical problem that leads to death when untreated. For surgeons and interventionalists, the optimal management of aortic dissections remains ill-defined. In the majority of patients currently suffering from aortic dissection, there is a pressing need for clinicians to have the appropriate information to accurately and predictably balance the risk of surgery versus the failure of medical management. Dissected aortas are mechanically unstable systems whose clinical behavior to date has been unpredictable. Traditional approaches to classifying dissections ignore the role of aortic geometry and its specific relation to aortic stability. Fully characterizing a given patient’s aortic fragility requires the integration of geometry, shape, and mechanics. Although computed tomography angiography (CTA) allows for unprecedented access to the details of aortic geometry, it remains largely ignored as surgeons are faced with making high-stake decisions to operate under emergent conditions of imminent clinical demise. Our 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. In the process, we will develop a biomechanical model of aortic stability on multiple time scales, allowing a mechanistic foundation for current clinical paradigms of aortic fragility. 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 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.