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通过结合CFD-FSI,cGAN和CNN来预测来自非侵入性图像的冠状动脉封闭风险

Mozhdeh Nikpour¹, Ali Mohebbi²

¹Department of Chemical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. mozhdehnikpour7@gmail.com.

Scientific reports

|September 30, 2024

概括

这项研究引入了一种新的AI方法,将CFD,FSI和深度学习结合起来,以从医学图像中预测冠状动脉封闭风险. 该方法准确评估风险水平,克服直接WSS测量的局限性.

科学领域:

心血管流体力学心血管流体力学
医学成像分析分析医学成像分析
医疗保健中的人工智能

背景情况:

壁切应力 (WSS) 对于评估血管封闭风险至关重要,但不能直接测量.
现有的测量方法存在分辨率低,不确定性高,成本高等问题.
准确和快速的风险预测对于有效的心血管疾病管理至关重要.

研究的目的:

开发一种新的,非侵入性的方法来预测冠状动脉封闭风险.
整合计算流体动力学 (CFD),流体结构相互作用 (FSI) 和深度学习 (cGAN,CNN) 进行WSS预测和风险分层.
用患者特定的MRI数据验证该方法.

主要方法:

开发了WSSGAN (条件生成对抗网络),通过CFD-FSI模拟进行训练,以预测WSS轮.
利用11层卷积神经网络 (CNN) 将预测的WSS轮分为风险等级 (低,中,高).
将患者的MRI扫描转换为3D几何,用于WSSGAN输入和随后的CNN风险分类.

主要成果:

通过WSSGAN模型成功预测了WSS轮.
通过CNN实现了冠状动脉封闭风险的准确分类,分为低,中,高等级.
通过使用患者的MRI数据,在现实情况下证明了该方法的适用性.

结论:

合并的CFD-FSI和深度学习方法为冠状动脉封闭风险预测提供了准确和快速的非侵入性方法.
这种新技术克服了直接WSS测量和传统成像方法的局限性.
开发的WSSGAN和CNN模型显示了在心血管诊断中临床应用的巨大潜力.

关键词:

在CFD-FSI交易中,心血管疾病的心血管疾病有条件的生成对抗网络 (cGAN)卷积神经网络 (CNN) 是一个神经网络.冠状动脉堵塞冠状动脉堵塞非侵入性的图像.

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