Lagrangian postprocessing of computational hemodynamics.

Lagrangian postprocessing of computational hemodynamics.
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DOI:
10.1007/s10439-014-1070-0
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发表时间:
2015-01
影响因子:
3.8
通讯作者:
Arzani, Amirhossein
Arzani, Amirhossein
中科院分区:
工程技术2区
文献类型:
--
作者:
Shadden, Shawn C.;Arzani, Amirhossein

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成像、建模和计算方面的最新进展迅速扩展了我们对大型血管(心脏、动脉和静脉)血流动力学进行建模的能力。这些数据编码了大量经常未得到充分利用的信息。对大血管中的血流进行建模(和测量)通常相当于求解感兴趣区域中的时变速度场。心脏和大动脉中的血流通常很复杂,速度场数据为研究血流动力学提供了起点。该数据可用于执行拉格朗日粒子跟踪和其他基于拉格朗日的后处理。如本文所述,拉格朗日方法对于从流体力学角度理解固有的瞬态血流动力学条件以及正确理解导致血管功能和健康的急剧和逐渐变化的生物力学因素是必要的。本文的目的是回顾用于心血管血流速度数据后处理的拉格朗日方法。
Recent advances in imaging, modeling and computing have rapidly expanded our capabilities to model hemodynamics in the large vessels (heart, arteries and veins). This data encodes a wealth of information that is often under-utilized. Modeling (and measuring) blood flow in the large vessels typically amounts to solving for the time-varying velocity field in a region of interest. Flow in the heart and larger arteries is often complex, and velocity field data provides a starting point for investigating the hemodynamics. This data can be used to perform Lagrangian particle tracking, and other Lagrangian-based postprocessing. As described herein, Lagrangian methods are necessary to understand inherently transient hemodynamic conditions from the fluid mechanics perspective, and to properly understand the biomechanical factors that lead to acute and gradual changes of vascular function and health. The goal of the present paper is to review Lagrangian methods that have been used in post-processing velocity data of cardiovascular flows.
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