Project Grant R01HL180406
- 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 (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 $722,299 Project Grant award from the National Heart, Lung, and Blood Institute (CFDA 93.837 - Cardiovascular Diseases Research) supports a research initiative to use deep learning techniques to study normal variation in aortic valve hemodynamics and its link to the development of aortic stenosis. The primary objectives are to: 1) Measure aortic valve parameters in 100,000 UK Biobank participants using deep learning models, validate the measurements at UCSF, and develop models to identify...
- This $188,136 Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), supports research to develop an Artificial Intelligence-Enabled Echocardiography Interpretation System (AEIS). The principal investigator, Dr. Chieh-Ju Chao, will train with a multidisciplinary mentoring team led by Drs. Bradley Erickson and Fei-Fei Li to advance his skills in visual-linguistic models and large language models. The goal...
- The University of Wisconsin-Milwaukee received a $298,509 project grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems under the Engineering federal grant program (CFDA 47.041). The three-year award will support research to enhance 4D-flow MRI through deep data assimilation for hemodynamic analysis of cardiovascular flows. Specifically, the University will collaborate on developing enhanced medical imaging techniques using data assimilation...
- The National Heart, Lung, and Blood Institute (NHLBI) awarded a $164,127 Project Grant under the Cardiovascular Diseases Research program (CFDA 93.837) to the University of Texas Health Science Center at Houston (UTHealth) to develop an artificial intelligence-based facial recognition tool for screening and early detection of heritable thoracic aortic disease (HTAD). The project aims to leverage facial image data from the Montalcino Aortic Consortium's patient registry to build a...
- This $1,302,060 federal Project Grant awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health (CFDA 93.286) program aims to develop an AI-assisted strategy for monitoring pulmonary congestion in acute heart failure patients in emergency settings. The primary goals are to automate lung ultrasound congestion scoring using AI/ML methods and a dataset of heart failure patients,...
- This federal Project Grant award, with a total funding amount of $304,415.00, was provided by the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837). The goal of this 1-year project is to develop a software solution that uses artificial intelligence and image analytics to improve cardiovascular risk prediction from screening CT calcium score (CTCS) images. The project team, which includes engineers and clinicians from...
- The National Heart, Lung, and Blood Institute (NHLBI) awarded a $443,544 Project Grant under the Cardiovascular Diseases Research (CFDA 93.837) program to Dasisimulations LLC, a minority-owned small business located in Dublin, Ohio. The grant supports the development and validation of an innovative, AI-driven software platform to automatically perform complex measurements for pre-planning transcatheter aortic valve replacement (TAVR) procedures. The project aims to (1) develop and validate the...
- The federal Project Grant award by the National Heart Lung and Blood Institute (CFDA 93.837 Cardiovascular Diseases Research) provides $534,761 to the University of California, Los Angeles (UCLA) to develop "Magnetoelastic Vascular Grafts" (MVGs). The MVGs are a transformative platform technology that can wirelessly and continuously monitor blood flow and detect stenosis (narrowing) in vascular grafts, a common issue affecting nearly 40% of grafts within 2 years of implantation. The...
This federal Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837) will fund the development of an end-to-end deep learning pipeline for highly efficient 4D flow MRI data acquisition and automated hemodynamic analysis in patients with bicuspid aortic valve (BAV) disease. The $704,234 award to Northwestern University will leverage the institution's large database of over 4,000 manually processed 4D flow MRI scans to train and validate the deep learning models. The project will also incorporate 4D flow data from the University of Washington and Columbia University to account for variability across MRI hardware vendors. The goal is to establish a streamlined, clinically translatable workflow for 4D flow MRI-based risk stratification of BAV patients, who are at risk of aortic complications like dilatation or dissection. The final deep learning networks and processing tasks will be embedded in the MRI scanners for on-scanner deployment.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 9/5/25 | ||
| Not listed | $704.2k | 8/4/25 |