FRACTAL GEOMETRIC ANALYSIS OF BRAIN MICROCIRCULATION

脑微循环的分形几何分析

• 批准号: 6319676
• 负责人: ANDRAS EKE
• 金额: $ 1.27万
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-12-16 至 1999-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6319676
• 关键词: FRACTAL; GEOMETRIC; ANALYSIS; BRAIN; MICROCIRCULATION

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 m odel of the observed pial and intraparenchymal crculation. 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.

所有机关的区域流动情况都有差异。 然而 这种变化不是随机的，因为存在相当大的相关性 在相邻区域的流动之间。 分形提供了一种衡量 变化和相关性，同时。 关键是 自相似性：表观变化的对数增加 与其中流动的组织样品的大小的对数成反比 是经过测量的。 两种不同的分形描述以前已经被 发现足以描述区域内流动的异质性 心脏和肺超过200倍的体素大小范围;一个是基于 一个基于统计自相似性，另一个基于自相似分支 算法 该项目的目标是开展联合研究， 以产生所需的数据， 大脑皮层与来自塞梅尔维斯的埃克博士合作 布达佩斯医科大学。 通过他的电脑视频成像 方法学、血流的实质内分布及其 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（216个微流数据 每幅图像）以进行分形分析。 将根据 统计自相似性，以确定空间 观察到的微流异质性的分形维数（Ds）， 64倍的体素尺寸范围。 我们希望获得更好的 了解红细胞和血浆的分形维数 脑实质内分布模式相互关联， 生理和病理条件。 因为艾克博士的方法 允许直接观察和覆盖软脑膜血管 网络上的微流图像，网格方法可以用来 测定所观察的软脑膜血管树的供应和排出的Ds 映射的组织区域。 获得这两个空间分形 维数提供了开发计算机模型的独特可能性。 观察到的软脑膜和脑实质内循环。 相互作用 在模型和实验之间，模型将被细化到 当它将模拟任何水平或截面的微流分布时， 的网络，并提供模拟的微流图像编译从 单个毛细管数据。 我们将使用该模型来扩展我们的 了解内部结构的拓扑结构， 相对于软脑膜动脉的脑实质内微血流分布 输入和静脉输出以及从软脑膜表面的深度。