FRACTAL GEOMETRIC ANALYSIS OF BRAIN MICROCIRCULATION

所有器官中都存在区域流动的变异性。 然而 这种变化不是随机的，因为存在相当大的相关性 相邻区域之间的流动。 分形提供了一种衡量 同时计算变化和相关性。 关键是 自相似性：表观变异的对数增加 与流动的组织样本大小的对数成反比 被测量。 之前有两种不同的分形描述 发现足以描述区域内流动的异质性 心脏和肺的体素大小范围超过 200 倍；一个是基于 一个是统计自相似性，另一个是自相似分支 算法。 该项目的目标是开展联合研究 产生有关器官内流量分布的所需数据 与 Semmelweis 的 Eke 博士合作的大脑皮层 布达佩斯医科大学。 通过他的计算机视频成像 方法论、实质内血流分布及其 成分、红细胞和血浆微流可以重复成像 在猫科动物和大鼠的大脑皮层中，导致微循环 足够空间分辨率的参数图像（216个微流数据 每个图像）进行分形分析。 它将基于 统计自相似性应用于确定空间 观察到的微流不均匀性 (Ds) 的分形维数 64 倍的体素尺寸范围。 我们期待获得更好的成绩 了解红细胞和血浆的分形维数 实质内分布模式在以下情况下彼此相关 生理和病理条件。 因为埃克博士的方法 允许直接观察和覆盖软脑膜血管 在微流图像上进行网络化，可以采用网格法 确定观察到的软脑膜血管树的供应和引流的 Ds 映射的组织区域。 访问这两个空间分形 尺寸提供了开发计算机模型的独特可能性 观察到的软脑膜和实质内循环。 通过互动 在模型和实验之间，模型将被细化到一定程度 当它将模拟任何级别或部分的微流分布时 网络并提供编译的模拟微流图像 单独的毛细管数据。 我们将使用该模型来扩展我们的 了解内部结构的拓扑 相对于软脑膜动脉的实质内微流分布 输入和静脉输出以及软脑膜表面的深度。

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, intraparenchymaldistribution 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.