Affiliation:
1. Beijing University of Technology, Beijing, China
2. Peking University People’s Hospital, Beijing, China
Abstract
This study developed a multi-input back-propagation neural network (BPNN) that can be used to predict coronary artery stenosis resistance by inputting vascular geometric parameters and blood flow. Compared with previous studies, the network developed in this study can accurately and rapidly predict coronary artery stenosis resistance, which can not only meet clinical requirements but also reduce the cost of calculation duration. This study contributes to the noninvasive methods for the numerical calculation of fractional flow reserve derived from coronary CT angiography (FFRCT) and indicates that this technique can potentially be used for evaluating myocardial ischemia.
Funder
Beijing Postdoctoral Science Foundation
China Postdoctoral Science Foundation
MOST | National Key Research and Development Program of China
National Natural Science Foundation of China
Publisher
American Physiological Society
Subject
Physiology (medical),Cardiology and Cardiovascular Medicine,Physiology
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献