Abstract
Micro-computed X-ray tomography (MicroCT) is one of the most powerful techniques available for the three-dimensional characterization of complex multi-phase or porous microarchitectures. The imaging and analysis of porous networks are of particular interest in tissue engineering due to the ability to predict various large-scale cellular phenomena through the micro-scale characterization of the structure. However, optimizing the parameters for MicroCT data capture and analyses requires a careful balance of feature resolution and computational constraints while ensuring that a structurally representative section is imaged and analysed. In this work, artificial datasets were used to evaluate the validity of current analytical methods by considering the effect of noise and pixel size arising from the data capture, and intrinsic structural anisotropy and heterogeneity. A novel ‘segmented percolation method’ was developed to exclude the effect of anomalous, non-representative features within the datasets, allowing for scale-invariant structural parameters to be obtained consistently and without manual intervention for the first time. Finally, an in-depth assessment of the imaging and analytical procedures are presented by considering percolation events such as micro-particle filtration and cell sieving within the context of tissue engineering. Along with the novel guidelines established for general pixel size selection for MicroCT, we also report our determination of 3 μm as the definitive pixel size for use in analysing connectivity for tissue engineering applications.
Highlights
A key desire in tissue engineering is to mimic existing native environments in order to effectively regenerate them
We explore ways in which the issues associated with percolation diameter extraction may be overcome through the ‘segmented percolation analysis’ method whereby the region of interest (ROI) is subdivided and analysed post data acquisition
Two measures of interconnectivity—the percolation diameter and volume interconnectivity—were evaluated for artificial Micro-computed tomography (MicroCT) datasets at various simulated pixel sizes. The analysis of these artificial lattices suggested that the mode of the pore size distribution within each lattice may be a more representative estimate than the mean for strongly anisotropic scaffolds, analysis on a large range of structures is required to validate this statement for a general structure
Summary
A key desire in tissue engineering is to mimic existing native environments in order to effectively regenerate them. Even though general rules may exist to understand the interdependence between pore size and processing conditions [1] scaffolds require significant structural characterization and analysis. As Liu et al described in 2011 [5] ‘the limitation of microtomography lies in the relationship of the length scale and resolution of the images’ if a higher resolution is desired typically a smaller length scale will be scanned This limitation can be extended further, at high resolution a ‘representative volume element’ [5] may consist of a dataset that is too large to be processed for percolation analyses given the constraints of computation placed by commercially available software, and scanning time. While the scientific literature generally reports the pixel size for any analysis within the methods sections, it has not been considered as a potential constraint in the case of analysis of pore size or measures of interconnectivity
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