Project Grant 2347472

The National Science Foundation (NSF) awarded a 3-year, $567,284 Project Grant under the Engineering program (CFDA 47.041) to Purdue University to develop a scalable Bayesian methodology for reconstructing cardiovascular hemodynamic flow fields and cardiac structure from advanced medical imaging modalities like phase-contrast MRI, 4D flow MRI, and color Doppler echocardiography. The research aims to overcome limitations in current imaging techniques, such as inaccurate velocity flow reconstruction and lack of quantified uncertainty, in order to provide clinicians with a more comprehensive and accurate picture of blood flow patterns to aid diagnosis, risk assessment, treatment planning, and management of heart disease. The project will formulate the inverse problem within an information field theory framework, explore parameterizations of time-varying hemodynamic fields, devise scalable numerical algorithms to characterize the joint posterior distributions, and validate the methodology using synthetic, in vitro, and in vivo data. The expected outcome is a next-generation statistical methodology that can solve the hemodynamics field and physical parameter estimation problem with quantified uncertainty, ultimately leading to more effective and personalized treatment plans for heart disease patients.