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PMID: 37303051 Published · epublish English Journal Article

A novel physics-based model for fast computation of blood flow in coronary arteries.

Biomedical engineering online ·Vol. 22 ·No. 1 ·2023-06-12 ·页码 56

Hu X, Liu X, Wang H, Xu L, Wu P, Zhang W, Niu Z, Zhang L, Gao Q

Abstract

Blood flow and pressure calculated using the currently available methods have shown the potential to predict the progression of pathology, guide treatment strategies and help with postoperative recovery. However, the conspicuous disadvantage of these methods might be the time-consuming nature due to the simulation of virtual interventional treatment. The purpose of this study is to propose a fast novel physics-based model, called FAST, for the prediction of blood flow and pressure. More specifically, blood flow in a vessel is discretized into a number of micro-flow elements along the centerline of the artery, so that when using the equation of viscous fluid motion, the complex blood flow in the artery is simplified into a one-dimensional (1D) steady-state flow. We demonstrate that this method can compute the fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA). 345 patients with 402 lesions are used to evaluate the feasibility of the FAST simulation through a comparison with three-dimensional (3D) computational fluid dynamics (CFD) simulation. Invasive FFR is also introduced to validate the diagnostic performance of the FAST method as a reference standard. The performance of the FAST method is comparable with the 3D CFD method. Compared with invasive FFR, the accuracy, sensitivity and specificity of FAST is 88.6%, 83.2% and 91.3%, respectively. The AUC of FFRFAST is 0.906. This demonstrates that the FAST algorithm and 3D CFD method show high consistency in predicting steady-state blood flow and pressure. Meanwhile, the FAST method also shows the potential in detecting lesion-specific ischemia.

Keywords
Computational fluid dynamics Coronary computed tomography angiography Fractional flow reserve Physics-based fast model
MeSH 主题词
Humans Coronary Vessels/diagnostic imaging Fractional Flow Reserve, Myocardial Heart Algorithms Physics
作者与单位
共 9 位作者,点击展开单位 / ORCID
Hu Xiuhua
Department of Radiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Liu Xingli
Hangzhou Shengshi Science and Technology Co., Ltd., Hangzhou, China.
Wang Hongping
The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing, China.
Xu Lei
Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Wu Peng
Biomanufacturing Research Centre, School of Mechanical and Electric Engineering, Soochow University, Suzhou, Jiangsu, China.
Zhang Wenbing
Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Niu Zhaozhuo
Department of Cardiac Surgery, Qingdao Municipal Hospital, Qingdao, China.
Zhang Longjiang
Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China. [email protected].
Gao Qi
Institute of Fluid Engineering, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China. [email protected].
Article Info
Journal
Biomedical engineering online
Abbr.
Biomed Eng Online
ISSN
1475-925X
Published
2023-06-12
电子出版
2023-00-12
页码
56
Language
English
Country/Region
England
NLM ID
101147518
基金资助
National Key Research and Development Program of China · 2017YFC0113400 for L.J.Z.
Key Program of the National Natural Science Foundation of China · No. 81830057 for L.J.Z.
National Natural Science Foundation of China · No. 12072320 for Q.G.
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