PFI (MCA): A Cardiac Imaging System
PFI (MCA): A Cardiac Imaging System
批准号:
2122299
负责人:
Pamela Bhatti
金额:
$37.69万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-01-01 至 2024-12-31
中文摘要
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英文摘要
The broader impact/commercial potential of this Partnerships for Innovation – Mid Career Advancement (PFI-MCA) project is a new system to improve imaging of heart (coronary) vessels for the detection and monitoring of coronary artery disease (CAD). CAD, the most common type of cardiovascular disease, is the leading cause of death in the U.S. for both men and women and responsible for 30% of deaths globally. CAD progresses by obstructing blood flow in heart vessels—coronary arteries. While catheter-based coronary angiography is considered the gold standard for assessing coronary arteries it is invasive, requiring insertion of a catheter for intra-arterial contrast agent injection for X-ray visualization of obstruction to blood flow. Catheter-based coronary angiography is also a costly ($57,500 per procedure), and over-utilized exam, with 40% of exams revealing normal arteries. As an alternative, computed tomography coronary angiography is a low cost and non-invasive imaging approach. However, computed tomography systems that are widely available in the U.S. must be effectively controlled to acquire diagnostic-quality scans with low radiation dose, i.e., image the heart when its motion is minimal. This new system will ensure safe, diagnostic-quality scans in all patients, providing widespread access to coronary imaging and eliminate invasive, unnecessary, costly procedures.The proposed project pursues cutting-edge research in cardiovascular imaging via aggressive technology development of real-time sensors and hardware. The project will enable real-time prediction of minimal heat motion (cardiac quiescence) thereby enabling diagnostic-quality computed tomography imaging of coronary vessels arteries by implementing an artificial neural network and a real-time hardware prototype. The system will prospectively predict quiescence based on external heart sounds and motion for individual patients on a beat-by-beat basis ready for adoption by computed tomography manufacturers. This will yield a significant advancement over existing strategies based on the electrocardiogram.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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