This award contributes to the national health, prosperity, and welfare by investigating the use of data-driven computer models and 3D printing technology to improve surgery planning for patients suffering from Aortic Stenosis. Aortic Stenosis is among the most common and severe valvular heart disease conditions and is generally addressed surgically through Transcatheter Aortic Valve Replacement (TAVR). Despite the overall survival benefit, the TAVR surgery may result in adverse outcomes such as paravalvular leakage, which is associated with hospital readmission, congestive heart failure, and increased one-year mortality. Computer models of a patient's valve may help improve surgical outcomes. The goal of this project is to produce a functional model of a heart valve in order to augment leakage data from real patients with experimental data based on 3D printed valves so as to develop more reliable predictive models for surgical planning. The research has the potential to significantly improve surgical outcomes in practice for cardiologic treatment and intervention and help reduce adverse outcomes of such surgeries. The award supports a graduate student whose research lies at the intersection of data science and additive manufacturing.
This project explores the idea of embedding microstructure in the 3D printed polymer to tailor its mechanical properties and mimic real human tissue. Finding the optimal configuration of the fiber insert is an extremely difficult problem because the optimization needs to be executed in an infinite dimensional functional domain. The project utilizes advanced statistical modeling techniques to make this optimization problem tractable. Specifically, a new functional input-functional output co-kriging model will be developed that relates the stress-strain curves to the fiber insert shapes. A key feature of this modeling technique is a new spectral distance correlation function that makes the model invariant to translations, which is a desirable property for the tissue mimicking problem. Furthermore, advanced techniques in experimental design and Bayesian model calibration are employed to obtain 3D printed valve data and merge them with available historical data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
