DT-MRI imagery of heart muscle to properly define muscle fibers. Fiber orientation is important to properly characterize directional mechanical response, as well as active contraction occurs along these fibers. Images courtesy of Center for Cardiovascular Simulation.
With a new, four-year, $3 million grant from the National Institutes of Health, ICES Professor Michael Sacks will lead a research team to develop advanced computational models that analyze characteristics directly from clinically-obtained imaging and predict how fast aortic valve disease will occur in cardiovascular patients.
This "personalized medicine" approach will first be applied to patients with
Biscuspid Aortic Valve Disease (BAV), the most common congenital heart defect. The condition, present at birth, affects approximately 2 percent of the population and is twice as common in men as women.
Normally the aortic valve, which directs the flow of blood away from the heart and to the entire body, has three leaflets that open and close together. In BAV however, the aortic valve is malformed and has two leaflets, which at least one is distorted and does not function effectively. Having a BAV puts patients at a higher risk for developing aortic valve disease, which can also reduce the efficiency of the heart.
Younger patients with BAV are much more likely to acquire aortic valve disease. Yet, some patients with BAV go through their entire lives unaffected, while others progress very rapidly. Currently no methods exist to determine clinically which patients with BAV will acquire aortic valve disease and how fast it will occur.
Computational simulations, using clinically derived data, can help to define how evolving biomechanical properties drive native and replacement valve function and performance. While it has become possible with advances in 3D-imaging modalities to have in-vivo valve geometric data available, optimal computational approaches to exploit such information and obtain functional states remains to be established.
Sacks will lead researchers at UT Austin, University of Pennsylvania, Iowa State University, and Columbia University to develop the advanced the computational models.
In close collaboration with UT's Texas Advanced Computing Center, Sacks' team will employ advanced computational biomechanics, high-resolution 3D reconstructions, and detailed structure-mechanics information of congenitally defective human heart valves. With this highly elaborate synthesis of data, they will develop a novel approach for estimating the mechanical behavior and deformations on patient-specific imaging data. All major computed and measured factors, such as organ level geometry, tissue structure and mechanical behavior, valve-mediated hemodynamics and wall shear stress patterns, and valve cell phenotypic shifts will be integrated into a detailed computational model. The results offer a unique ability to both understand and correlate key pathological aspects of the BAV pathology.
"There's a clear personalized medicine component to our project," says Sacks, professor of biomedical engineering, director of the ICES Willerson Center for Cardiovascular Modeling and Simulation, and holder of the W.A. Tex Moncrief, Jr. Endowment in Simulation-Based Engineering and Sciences Chair I. "The overall goal of the project is to identify geometric features that lead to a high risk of aortic stenosis development based on a thorough understanding of patient-specific BAV characteristics unique to the disorder."