A variety of diseases can lead to loss of lung tissue. Currently, this can be treated only symptomatically. In mice, a complete compensatory lung growth within 21 days after resection of the left lung can be observed. Understanding and transferring this concept of compensatory lung growth to humans would greatly improve therapeutic options. Lung growth is always accompanied by a process called angiogenesis forming new capillary blood vessels from preexisting ones. Among the processes during lung growth, the formation of transluminal tissue pillars within the capillary vessels (intussusceptive pillars) is observed. Therefore, pillars can be understood as an indicator for active angiogenesis and microvascular remodelling. Thus, their detection is very valuable when aiming at characterization of compensatory lung growth. In a vascular corrosion cast, these pillars appear as small holes that pierce the vessels. So far, pillars were detected visually only based on 2D images. Our approach relies on high-resolution synchrotron microcomputed tomographic images. With a voxel size of 370 nm we exploit the spatial information provided by this imaging technique and present the first algorithm to semiautomatically detect intussusceptive pillars. An at least semiautomatic detection is essential in lung research, as manual pillar detection is not feasible due to the complexity and size of the 3D structure. Using our algorithm, several thousands of pillars can be detected and subsequently analysed, e.g. regarding their spatial arrangement, size and shape with an acceptable amount of human interaction. In this paper, we apply our novel pillar detection algorithm to compute pillar densities of different specimens. These are prepared such that they show different growing states. Comparing the corresponding pillar densities allows to investigate lung growth over time.

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Cirrhosis describes the development of excess fibrous tissue around regenerative nodules in response to chronic liver injury and usually leads to irreversible organ damage and end-stage liver disease. During the development of cirrhosis, the formation of collagenous scar tissue is paralleled by a reorganization and remodeling of the hepatic vascular system. To date, macrovascular remodeling in various cirrhosis models has been examined using three-dimensional (3D) imaging modalities, while microvascular changes have been studied mainly by two-dimensional (2D) light microscopic and electron microscopic imaging. Here, we report on the application of high-resolution 3D synchrotron radiation-based microtomography (SRμCT) for the study of the sinusoidal and capillary blood vessel system in three murine models of advanced parenchymal and biliary hepatic fibrosis. SRμCT facilitates the characterization of microvascular architecture and identifies features of intussusceptive angiogenesis in progressive liver fibrosis in a non-destructive 3D manner.

A stochastic microstructure model based on a random Laguerre tessellation is used to simulate virtual open cell foam structures. Both circular cylindric and concave triangular strut cross section shapes are considered. Additional geometry modifications are introduced by relaxation of the tessellation cells using the Surface Evolver software and by closing a certain percentage of the foam windows. The effect of these modifications on the foams' permeabilities is investigated. In particular, permeability anisotropies resulting from anisotropic closing of the windows are taken into account. The dimensionless permeability (Darcy number) in the different directions is well explained by regression models using porosity, geometric tortuosity, and constrictivity as explanatory variables.

Image filtering is one of the most common and important tasks in image processing applications. In this paper, image processing using a mean filtering algorithm combined with thresholding and binarization algorithms for the 3D visualization and analysis of murine lungs is explained. These algorithms are then mapped on the Maxler's MAX2336B Dataflow Engine (DFE) to significantly increase calculation speed. Several different DFE configurations were tested and each yielded different performance characteristics. Optimal algorithm calculation speed was up to 30 fold baseline calculation speed