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.
This work was partially supported by grants n° 00 H 0587 from the Fonds de la Recherche Technologique of the French Department of Research and Technology and n° 02 TS 031 of the French Department of Education Research and Technology. The authors are greatly indebted to Henri Duvernoy who placed his wonderful collection of injected human brains at our disposal and gave us precious indications. We thank Philippe Cochard and Brice Ronsin for their help in the use of confocal microscopy at the Centre de Biologie du Développement of Université Paul Sabatier, Toulouse, and Grégoire Malandain at INRIA and Malte Westerhoff from Mercury Inc., for their help in the development on the central lines extraction algorithms. The authors also express their thanks to Prasanna Puwanarajah for revising the English language of the manuscript.