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...
This $760,046 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a computationally-efficient multiscale modeling framework that integrates machine learning and artificial intelligence to predict structural and functional changes in the heart due to disease progression. The project aims to build fundamental understanding of heart disease by combining techniques from...
This $200,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will fund the development of advanced computational methods to create high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. The project aims to deliver: 1) novel machine learning algorithms for accurate digital twin geometry reconstruction from 3D medical images, 2) an efficient inverse method to identify in vivo...
This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $439,954 to Michigan State University (MSU) to develop advanced computational models of the heart that leverage machine learning and artificial intelligence. The goal is to create a multiscale modeling framework that can predict structural and functional changes in the heart due to disease conditions like pathological fibrosis. The...
This $262,138 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports research at the University of Houston to investigate the mechanisms of cardiac fibrosis, a type of scarring in the heart that leads to impaired function. The key objectives are to: 1) determine the role of the endothelial-mesenchymal transition in cardiac fibrosis, and 2) develop a 3D cardiac fibrosis model to study tissue remodeling. The research will leverage a...
The National Science Foundation awarded a $279,606 Project Grant to the University of California Irvine under the Engineering federal grant program (CFDA 47.041). The grant supports collaborative research to develop multiscale computational models characterizing how heart muscle cells adapt mechanically to changes in load conditions. Through experimental measurement and multi-scale modeling, the university researchers will elucidate the acute functional response and long-term remodeling response...
This National Science Foundation (NSF) Engineering (CFDA 47.041) program grant, in the amount of $400,000, supports the development of a tissue-like, converged sensing platform for tracking excitation-contraction dynamics in cardiac organoids. The project aims to: Develop a scalable assembly strategy to fabricate an array of three-dimensional sensor structures using planar semiconducting graphene material, designed to detect both electrical and mechanical stimuli. Evaluate the multifunctional...
This National Science Foundation (NSF) Project Grant award under CFDA 47.041 (Engineering) supports the development of a new computational model to predict the complex interactions between ventricular assist devices (VADs) and the beating native heart. The goal is to create a "digital twin" of an experimental mock circulatory loop used for testing VAD safety and efficacy. This $195,291 award, effective August 1, 2024 through July 31, 2026, will result in a lumped parameter model that...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program totaling $696,331 will support the development of a new method to quantify cardiac performance using routine MRI scans. The University of Central Florida will work with collaborators to overcome obstacles hindering the clinical deployment of measuring heart muscle fiber strain. Researchers will combine computational modeling and artificial intelligence with readily available...
This four-year, $1.1 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund the development of physics-informed machine learning techniques to analyze dynamic blood flow from static subtraction computed tomographic angiography imaging. The University of Wisconsin-Milwaukee will train graduate students in deep learning methods and engage undergraduates and local high school students, particularly those from...
The National Science Foundation awarded a $542,441 Project Grant to the Texas A&M Engineering Experiment Station to develop a validated hybrid echocardiography-computational fluid dynamics framework for patient-specific cardiac assessment under the Engineering (47.041) federal grant program. The project aims to create a computational pipeline using standard echocardiography scans to model cardiac flow and function, and compute in vivo mechanical and electrophysiological properties for specific patients. Researchers will use deep learning to identify heart chambers and valves from echocardiography images and reconstruct the 3D geometry. An immersed boundary computational fluid dynamics code will simulate large deformation fluid-structure interaction to validate the framework using animal studies and retrospective clinical scans. Mayo Clinic Arizona was awarded a subgrant under the project to contribute to developing the validated hybrid modeling framework for improved cardiac assessment and disease understanding.