Nair Malavika, Shepherd Jennifer H, Best Serena M, Cameron Ruth E
Cambridge Centre for Medical Materials, Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, UK.
School of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK.
J R Soc Interface. 2020 Apr;17(165):20190833. doi: 10.1098/rsif.2019.0833. Epub 2020 Apr 22.
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.
微计算机断层扫描（MicroCT）是用于对复杂多相或多孔微结构进行三维表征的最强大技术之一。由于能够通过结构的微观尺度表征来预测各种大规模细胞现象，多孔网络的成像和分析在组织工程中特别受关注。然而，优化MicroCT数据采集和分析的参数需要在特征分辨率和计算约束之间仔细权衡，同时确保对具有结构代表性的切片进行成像和分析。在这项工作中，通过考虑数据采集产生的噪声和像素大小以及固有结构各向异性和异质性的影响，使用人工数据集来评估当前分析方法的有效性。开发了一种新颖的“分段渗流方法”，以排除数据集中异常、非代表性特征的影响，从而首次能够一致地获得尺度不变的结构参数，且无需人工干预。最后，通过在组织工程背景下考虑诸如微粒过滤和细胞筛分等渗流事件，对成像和分析程序进行了深入评估。除了为MicroCT的一般像素大小选择制定的新指南外，我们还报告了我们确定3μm为用于分析组织工程应用连通性的确定像素大小。
