The studies of mammalian vasculature are an essential part of biomedical research, enabling the development of physiological understanding and forming the background of medical techniques and therapy. Despite the fact that the basic principles of vessel network description were established in the first quarter of the twentieth century, a digital model describing the vasculature in full accordance with experimental data has not yet been created. In the present study, we combine the determined structure design of basic arterial vessels with the stochastic creation of small vessel networks. By the example of rat brain arterial network model it was shown that the arterial blood volume and the magnitude of the blood flow impose a limitation on the network architecture. In particular, the bifurcation exponent (γ) should not be less than 2.7, and the optimal value of this parameter lies in the range of 2.9-3.0. Although the networks with a low γ appear as branched and complex, they do not fill out the phantom properly. Thus, the architecture of the vasculature is fundamentally determined by topological geometrical parameters.

中文翻译：

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鼠脑血管系统的重建揭示了血管网络结构特性的基本限制。

哺乳动物脉管系统的研究是生物医学研究的重要组成部分，有助于发展生理理解并形成医学技术和疗法的背景。尽管血管网络描述的基本原理是在20世纪第一季度建立的，但尚未完全建立根据实验数据描述脉管系统的数字模型。在本研究中，我们将确定的基础动脉血管结构设计与随机创建的小血管网络相结合。以大鼠脑动脉网络模型为例，表明动脉血容量和血流量对网络结构构成了限制。特别是，分叉指数（γ）不得小于2.7，该参数的最佳值在2.9-3.0的范围内。尽管具有低γ的网络看起来像分支且复杂，但它们无法正确填充幻像。因此，脉管系统的结构基本上由拓扑几何参数确定。