Hierarchical microimaging for multiscale analysis of large vascular networks

Heinzer, Stefan, Krucker, Thomas, Stampanoni, Marco, Abela, Rafael, Meyer, Eric P., Schuler, Alexandra, Schneider, Philipp and Müller, Ralph (2006) Hierarchical microimaging for multiscale analysis of large vascular networks. NeuroImage, 32 (2), 626-636. (doi:10.1016/j.neuroimage.2006.03.043).

Abstract

There is a wide range of diseases and normal physiological processes that are associated with alterations of the vascular system in organs. Ex vivo imaging of large vascular networks became feasible with recent developments in microcomputed tomography (µCT). Current methods permit to visualize only limited numbers of physically excised regions of interests (ROIs) from larger samples. We developed a method based on modified vascular corrosion casting (VCC), scanning electron microscopy (SEM), and desktop and synchrotron radiation µCT (SRµCT) technologies to image vasculature at increasing levels of resolution, also referred to as hierarchical imaging. This novel approach allows nondestructive 3D visualization and quantification of large microvascular networks, while retaining a precise anatomical context for ROIs scanned at very high resolution. Scans of entire mouse brain VCCs were performed at 16-µm resolution with a desktop µCT system. Custom-made navigation software with a ROI selection tool enabled the identification of anatomical brain structures and precise placement of multiple ROIs. These were then scanned at 1.4-µm voxel size using SRµCT and a local tomography setup. A framework was developed for fast sample positioning, precise selection of ROIs, and sequential high-throughput scanning of a large numbers of brain VCCs. Despite the use of local tomography, exceptional image quality was achieved with SRµCT. This method enables qualitative and quantitative assessment of vasculature at unprecedented resolution and volume with relatively high throughput, opening new possibilities to study vessel architecture and vascular alterations in models of disease.

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Contributors

Author: Philipp Schneider

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