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一种用于定量研究人类大脑皮质微血管网络的新型三维计算机辅助方法。

A novel three-dimensional computer-assisted method for a quantitative study of microvascular networks of the human cerebral cortex.

作者信息

Cassot Francis, Lauwers Frederic, Fouard Céline, Prohaska Steffen, Lauwers-Cances Valerie

机构信息

Functional Neuroimaging Laboratory, INSERM U455, CHU Purpan, Toulouse, France.

出版信息

Microcirculation. 2006 Jan;13(1):1-18. doi: 10.1080/10739680500383407.

Abstract

OBJECTIVE

Detailed information on microvascular network anatomy is a requirement for understanding several aspects of microcirculation, including oxygen transport, distributions of pressure, and wall shear stress in microvessels, regulation of blood flow, and interpretation of hemodynamically based functional imaging methods, but very few quantitative data on the human brain microcirculation are available. The main objective of this study is to propose a new method to analyze this microcirculation.

METHODS

From thick sections of india ink-injected human brain, using confocal laser microscopy, the authors developed algorithms adapted to very large data sets to automatically extract and analyze center lines together with diameters of thousands of brain microvessels within a large cortex area.

RESULTS

Direct comparison between the original data and the processed vascular skeletons demonstrated the high reliability of this method and its capability to manage a large amount of data, from which morphometry and topology of the cerebral microcirculation could be derived.

CONCLUSIONS

Among the many parameters that can be analyzed by this method, the capillary size, the frequency distributions of diameters and lengths, the fractal nature of these networks, and the depth-related density of vessels are all vital features for an adequate model of cerebral microcirculation.

摘要

目的

微血管网络解剖的详细信息是理解微循环多个方面的必要条件,这些方面包括氧气运输、微血管中的压力分布和壁面剪应力、血流调节以及基于血流动力学的功能成像方法的解读,但关于人脑微循环的定量数据非常少。本研究的主要目的是提出一种分析这种微循环的新方法。

方法

作者从注入印度墨水的人脑厚切片中,使用共聚焦激光显微镜,开发了适用于非常大的数据集的算法,以自动提取和分析大皮质区域内数千条脑微血管的中心线和直径。

结果

原始数据与处理后的血管骨架之间的直接比较证明了该方法的高可靠性及其处理大量数据的能力,从中可以得出脑微循环的形态学和拓扑学信息。

结论

在可以通过该方法分析的众多参数中,毛细血管大小、直径和长度的频率分布、这些网络的分形性质以及与深度相关的血管密度都是建立适当的脑微循环模型的重要特征。

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