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数字化血管网络中血流的格子玻尔兹曼模拟

Lattice Boltzmann simulation of blood flow in digitized vessel networks.

作者信息

Sun Chenghai, Munn Lance L

机构信息

Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114,

出版信息

Comput Math Appl. 2008 Apr;55(7):1594-1600. doi: 10.1016/j.camwa.2007.08.019.

Abstract

Efficient flow of red blood cells (RBCs) and white blood cells (WBCs) through the microcirculation is necessary for oxygen and nutrient delivery as well as immune cell function. Because blood is a dense particulate suspension, consisting of 40% RBCs by volume, it is difficult to analyze the physical mechanisms by which individual blood cells contribute to the bulk flow properties of blood. Both experimental and computational approaches are hindered by these non-Newtonian properties, and predicting macroscopic blood flow characteristics such as viscosity has historically been an empirical process. In order to examine the effect of the individual cells on macroscopic blood rheology, we developed a lattice Boltzmann model that considers the blood as a suspension of particles in plasma, accounting explicitly for cell-cell and cell-wall interactions. Previous studies have concluded that the abundance of leukocyte rolling in postcapillary venules is due to interactions between red blood cells and leukocytes as they enter postcapillary expansions. Similar fluid dynamics may be involved in the initiation of rolling at branch points, a phenomenon linked to atherosclerosis. The lattice Boltzmann approach is used to analyze the interactions of red and white blood cells as they flow through vascular networks digitized from normal and tumor tissue. A major advantage of the lattice-Boltzmann method is the ability to simulate particulate flow dynamically and in any geometry. Using this approach, we can accurately determine RBC-WBC forces, particle trajectories, the pressure changes in each segment that accompany cellular traffic in the network, and the forces felt by the vessel wall at any location. In this technique, intravital imaging using vascular contrast agents produces the network outline that is fed to the lattice-Boltzmann model. This powerful and flexible model can be used to predict blood flow properties in any vessel geometry and with any blood composition.

摘要

红细胞(RBC)和白细胞(WBC)在微循环中高效流动对于氧气和营养物质的输送以及免疫细胞功能至关重要。由于血液是一种浓稠的颗粒悬浮液,按体积计由40%的红细胞组成,因此很难分析单个血细胞对血液整体流动特性产生影响的物理机制。实验和计算方法都受到这些非牛顿特性的阻碍,并且预测宏观血流特性(如粘度)在历史上一直是一个经验过程。为了研究单个细胞对宏观血液流变学的影响,我们开发了一种格子玻尔兹曼模型,该模型将血液视为血浆中颗粒的悬浮液,明确考虑了细胞间和细胞与壁之间的相互作用。先前的研究得出结论,毛细血管后微静脉中白细胞滚动的丰富程度是由于红细胞和白细胞进入毛细血管后扩张时的相互作用。类似的流体动力学可能参与了分支点处滚动的起始,这一现象与动脉粥样硬化有关。格子玻尔兹曼方法用于分析红细胞和白细胞在流经从正常组织和肿瘤组织数字化的血管网络时的相互作用。格子玻尔兹曼方法的一个主要优点是能够动态模拟任何几何形状中的颗粒流动。使用这种方法,我们可以准确确定红细胞与白细胞之间的力、颗粒轨迹、网络中细胞流动时每个节段的压力变化以及血管壁在任何位置所感受到的力。在这项技术中,使用血管造影剂的活体成像产生网络轮廓,该轮廓被输入到格子玻尔兹曼模型中。这个强大且灵活的模型可用于预测任何血管几何形状和任何血液成分下的血流特性。

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