FRACTAL GEOMETRIC ANALYSIS OF BRAIN MICROCIRCULATION

所有机关的区域流动情况都有差异。然而这 变化不是随机的，因为它们之间存在相当大的相关性。 在邻近地区流动。分形提供了一种测量 的相关性，同时。关键是自相似性：日志 表观变异的增加与尺寸的对数成反比 在其中测量流量的组织样本的。两种不同分形 先前已经发现描述足以描述 心脏和肺内区域流动的异质性超过200倍 体素大小的范围;一个是基于统计自相似性， 另一个是自相似分支算法。本项目的目标 进行联合研究，以获得所需的器官内数据， 大脑皮层中的血流分布与Eke博士合作 位于布达佩斯的塞麦尔维斯医科大学。通过他的电脑化 影像学方法，血流的实质内分布， 其成分、红细胞和血浆微流可重复成像， 在猫和大鼠的大脑皮层中， 足够空间分辨率的参数图像（每个 图像）进行分形分析。它将基于 应用统计自相似性来确定空间 观察到的微流异质性（Ds）的分形维数内， 64-体素大小的折叠范围。我们希望能更好地了解 红细胞和血浆在脑实质内的分形维数 分布模式在生理和 病理条件。因为Eke博士的方法可以直接 软脑膜血管网的观测及其在微流场上的叠加 图像，网格法可以用来确定所观察到的软膜的Ds 血管树供应和引流映射的组织区域。获得 这两个空间分形维数提供了独特的可能性， 开发所观察软脑膜和脑实质内的计算机模型 流通通过模型与实验的相互作用， 被细化到当它将模拟微流分布时， 并提供模拟的微流 从单个毛细血管数据编译的图像。我们将使用该模型来 扩展我们对内部结构拓扑的理解， 相对于软脑膜动脉输入的脑实质内微流分布 以及静脉输出和从软脑膜表面的深度。

Variability in regional flows is found in all organs. However this variation is NOT random, for there is considerable correlation between flows in neighboring regions. Fractals provide a measure of the variation AND the correlation, simultaneously. The key is self-similarity: the log of the apparent variation increases inversely with the log of the size of the tissue samples in which flow is measured. Two different fractal descriptions have been previously found adequate to describe the heterogeneity of regional flows within the heart and lung over a 200-fold range of voxel sizes; one was based on statistical self similarity, the other on a self-similar branching algorithm. The goals of this project are to carry out joint studies to yield needed data about the intraorgan flow distribution in the brain cortex in cooperation with Dr. Eke from the Semmelweis University of Medicine in Budapest. By his computerized videoimaging methodology, intraparenchymal distribution of blood flow and its components, red cell and plasma microflow can be repetitively imaged in the feline and rat brain cortex resulting in microcirculatory parameter images of adequate spatial resolution (216 microflow data per image) for fractal analysis to be carried out. It will be based on statistical self similarity to be applied to determine the spatial fractal dimension of the observed microflow heterogeneity (Ds) within a 64-fold range of voxel sizes. We expect to gain a better understanding of how the fractal dimensions of red cell's and plasma's intraparenchymal distribution patterns relate to each other under physiological and pathological conditions. Because Dr. Eke's method allows for direct observation and overlaying of the pial vascular network on the microflow images, the grid method can be used to determine Ds of the observed pial vascular tree supplying and draining the mapped tissue area. Access to these two spatial fractal dimensions offers the unique possibility to develop a computer model of the observed pial and intraparenchymal circulation. By interaction between model and experiment, the model will be refined to the point when it will simulate microflow distributions at any level or section of the network and to provide simulated microflow images compiled from individual capillary data. We will use the model to extend our understanding of the topology of the internal structure of intraparenchymal microflow distributions relative to pial arterial inputs and venous outputs and depth from the pial surface.