Computational fluid dynamics modelling in cardiovascular medicine.
Computational fluid dynamics modelling in cardiovascular medicine.
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DOI：
10.1136/heartjnl-2015-308044
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Gunn JP
中科院分区：
文献类型：
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作者：
Morris PD;Narracott A;von Tengg-Kobligk H;Silva Soto DA;Hsiao S;Lungu A;Evans P;Bressloff NW;Lawford PV;Hose DR;Gunn JP
本文综述了心血管医学中采用和翻译计算流体动力学（CFD）建模的方法、益处和挑战。CFD是数学的一个专业领域，也是流体力学的一个分支，通常用于各种安全关键工程系统，越来越多地应用于心血管系统。通过促进快速，经济，低风险的原型设计，CFD建模已经彻底改变了支架，瓣膜假体和心室辅助装置等设备的研究和开发。结合心血管成像，CFD模拟可以详细描述复杂的生理压力和流场，并计算无法直接测量的指标，例如壁面剪切应力。CFD模型目前正被转化为临床工具，供医生用于冠状动脉、瓣膜、先天性、心肌和外周血管疾病。CFD建模适用于微创患者评估。患者特异性（包含个体独有的数据）和多尺度（结合不同长度和时间尺度的模型）建模可以实现个性化风险预测和虚拟治疗计划。这与传统上依赖基于登记册的人口平均数据的做法有很大不同。模型集成正逐步向“数字患者”或“虚拟生理人”表示发展。当与人群规模的数值模型相结合时，这些模型有可能降低与临床试验相关的成本，时间和风险。CFD建模的采用标志着心血管医学的新时代。虽然可能非常有益，但一些学术和商业团体正在解决相关的方法、监管、教育和服务方面的挑战。
This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards ‘digital patient’ or ‘virtual physiological human’ representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.