基于计算血流动力学的小型腹主动脉瘤生长预测:层流模拟与大涡流模拟。

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL
Mostafa Rezaeitaleshmahalleh, Zonghan Lyu, Nan Mu, Min Wang, Xiaoming Zhang, Todd E. Rasmussen, Robert D. McBane II, Jingfeng Jiang
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引用次数: 0

摘要

先前的研究表明,计算流体动力学(CFD)模拟有助于评估腹主动脉瘤(AAA)患者的特异性血液动力学;患者的特异性血液动力学压力因素经常被用来预测 AAA 的生长。以往的研究利用层流和湍流模拟模型模拟血液动力学。然而,不同的 CFD 模拟模型对 AAA 生长预测模型的影响仍是未知数,这也是本研究的知识缺口。具体来说,我们对 70 个已知生长状态(即快速生长[> 5 mm/年]或缓慢生长[< 5 mm/年])的 AAA 模型进行了 CFD 模拟,这些模型来自 70 名患者的计算机断层扫描血管造影(CTA)数据。我们使用层流和大涡流模拟(LES)模型获取血液动力学参数,以预测 AAA 的生长状态。根据形态学、血液动力学和患者健康参数,结合三种经典的机器学习(ML)分类器,即支持向量机(SVM)、K-近邻(KNN)和广义线性模型(GLM),预测 AAA 的生长状态。我们的初步结果估计,层流和 LES 流动模拟中的动脉瘤流动稳定性和壁剪应力(WSS)相当。此外,通过层流和 LES 模拟计算得到的 WSS 和速度相关血流动力学变量在区分 AAA 的生长状态方面表现出相当的能力。更重要的是,上述三种 ML 分类器的预测建模性能相似,观察到的差异不到 2%(P 值 > 0.05)。总之,我们的研究结果表明,就所调查的数据而言,两种不同的血流模拟并不会对计算血流动力学和 AAA 生长状态预测建模的结果产生显著影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA’s growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients’ computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5 mm/yr] or slowly growing [< 5 mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs’ growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value > 0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs’ growth status, given the data investigated.

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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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