FRACTAL GEOMETRIC ANALYSIS OF BRAIN MICROCIRCULATION

在所有器官中都发现了区域流动的可变性。然而，这是 变化不是随机的，因为两者之间有相当大的相关性 在邻近地区流动。分形图提供了一种变化的度量 以及相互关系，同时。关键是自相似：日志 的表观变化与大小的对数成反比增加 在其中测量血流的组织样本。两种不同的分形 以前已经发现，描述足以描述 心肺内区域血流的异质性超过200倍 体素大小的范围；一个是基于统计自相似性，即 另一种是关于自相似分支算法的。这个项目的目标是 进行联合研究，以获得所需的器官内数据 与Eke博士合作的大脑皮层血流分布 布达佩斯的塞梅尔维斯医科大学。由他的计算机化 视频成像方法，实质内血流分布和 它的成分、红细胞和血浆微流量可以重复成像 在猫和大鼠的大脑皮质中导致微循环 具有足够空间分辨率的参数图像(每个微流数据216个 图像)，用于进行分形图分析。它将基于 应用统计自相似来确定空间 观测到的微流非均质性(D)的分维 -体素大小的折叠范围。我们希望能更好地了解 红细胞和血浆实质内的分维 生理和生理条件下的分布格局相互关联 病理情况。因为Eke博士的方法允许直接 软脑膜血管网在微循环上的观察和叠加 图像中，网格法可以用来确定观察到的软脑膜的DS 维管树对标测的组织区域进行供应和排水。进入 这两个空间分维提供了独特的可能性 建立观察到的软脑膜和实质内的计算机模型 发行量。通过模型与实验的相互作用，模型将 被细化到可以模拟微流分布的程度 网络的任何级别或部分，并提供模拟的微流 由单个毛细血管数据汇编而成的图像。我们将使用该模型来 扩展我们对内部结构的拓扑的理解 脑实质内微血流相对于软脑膜动脉输入的分布 软膜表面的静脉输出量和深度。

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.