Project Grant 2205103
Award Date
9/1/22
Completion Date
8/31/26
Dollars Obligated
$454K
Funding Federal Agency
Division of Computing and Communication Foundations
Awarding Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Palo Alto, CA 94304, USA
Similar Awards
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 $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 National Science Foundation (NSF) Project Grant award, provided under CFDA Program 47.041 (Engineering), aims to delineate and model the intra- and extra-cellular mechanisms contributing to right ventricular (RV) myocardial stiffening. The $373,596 award, effective March 1, 2025 through February 29, 2028, will utilize a combination of experimental and computational approaches to enhance understanding of RV physiology and diastolic dysfunction. Experimentally, the project will conduct...
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 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...
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...
This $512,000 National Science Foundation project grant to Angioinsight, Inc. supports the development of reduced order modeling methods to estimate fractional flow reserve (FFR) values using angiographic data. The NSF Division of Industrial Innovation awarded this grant under the Engineering program (CFDA 47.041) to advance the diagnosis of coronary artery disease. Specifically, the one-year project beginning June 15, 2022 will develop and calibrate a reduced order model leveraging graph theory...
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 federal Project Grant award, provided by the National Science Foundation's Engineering program (CFDA 47.041), aims to enhance the understanding of the mechanisms that drive cardiac arrhythmias, with the goal of improving both diagnosis and treatment effectiveness. The project, awarded on February 1, 2025 for $418,901, integrates complex computational simulations of various physical phenomena with advanced deep-learning techniques to explore how multiple physiological factors within the...
The National Science Foundation (NSF) awarded a $174,990 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program to Yeshiva University. The award, effective September 15, 2024 through August 31, 2026, aims to develop novel deep learning models for the automated detection of cardiomegaly (heart enlargement) in animals. The project seeks to bridge clinical metrics used by veterinarians with deep learning techniques to improve the trustworthiness...