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 $252,456 National Science Foundation Project Grant supports the development of generative deep learning algorithms and a data-driven approach to produce additional high-resolution information from low-resolution optical coherence tomography images of coronary arteries. Funded under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), this research aims to generate new super-resolution and cross-modality image translation techniques for improving pathological...
This $1,199,991 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop novel technologies for real-time assessment of blood clot properties to improve stroke treatment. The key products and services under this grant include: Integration of a sub-millimeter Raman fiber probe into a catheter for intravascular, in-vivo measurement of clot chemical composition. Training of a convolutional neural network to...
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...
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 federal Project Grant award for $220,000, provided by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), aims to deliver a comprehensive mathematical and computational framework for optimizing the design of next-generation stents. Key products and services to be provided under this 3-year award (August 1, 2024 to July 31, 2027) include: Developing a reduced model optimization module for the optimal...
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...
The National Science Foundation (NSF) awarded a $1,000,000 Project Grant under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to the Research Foundation of the City University of New York (RFCUNY) for the "PARTNER: AI/ML-DRIVEN EDGE COMPUTING FOR CARDIOVASCULAR DISEASE DIAGNOSIS/MECHANISM STUDY" project. This collaborative research effort between RFCUNY's City College of New York (CCNY) and the AI Institute for Future Edge Networks and Distributed Intelligence...
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...