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用于计算性血管流量建模的加速模拟方法

Michael MacRaild^1,2, Ali Sarrami-Foroushani^1,3, Toni Lassila^1,4

¹Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK.

Journal of the Royal Society, Interface

|February 13, 2024

概括

本综述探讨了加速血管流量建模的方法,这对于了解疾病和医疗器械至关重要. 它强调了减少订单建模和机器学习技术,以实现更快,更高效的模拟.

科学领域:

计算流体动力学计算流体动力学
生物医学工程生物医学工程
医疗器械开发医疗器械开发

背景情况:

血管流量建模对于理解血管病理和设计医疗器械至关重要.
基于纳维埃-斯托克斯方程的当前模型由于多物理和多尺度的复杂性而具有计算密集性.
这种复杂性导致高成本和过度的模拟时间,阻碍了实际应用.

研究的目的:

审查和分析用于计算血管流量建模的加速模拟方法.
评估各种减少顺序建模 (ROM) 和机器学习 (ML) 技术在加速血管流动模拟中的适用性和有效性.
确定挑战,并提出未来的研究方向,以优化血管流量建模.

主要方法:

对减少顺序建模 (ROM) 技术的审查,包括基于零/一维和模式分解的方法.
探索机器学习 (ML) 方法,如ML增强的ROM,基于ML的ROM和基于物理的ML模型.
讨论每个加速方法的优势,局限性和特定领域的挑战.

主要成果:

各种ROM和ML技术显示出加速血管流动模拟的前景.
每种方法都有独特的优点和缺点,取决于具体的应用和解剖学复杂性.
对不同的血管流量模拟加速应用分析了精度和加速度因素.

结论:

加速模拟方法对于推进血管流量建模至关重要.
加速技术的选择取决于血管流量问题的具体要求.
未来的研究应该专注于多尺度加速方法,以解决复杂血管几何结构中的几何变异性.

关键词:

血液动力学血液动力学机器学习是机器学习.减少订单建模减少订单建模模拟加速加速的模拟血管流量建模的模型.

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