Effect of data binning and frame averaging for micro-CT image acquisition on the morphometric outcome of bone repair assessment

A new bin size index method for statistical analysis of multimodal datasets from materials characterization

This paper presents a normalized standard error-based statistical data binning method, termed "bin size index" (BSI), which yields an optimized, objective bin size for constructing a rational histogram to facilitate subsequent deconvolution of multimodal datasets from materials characterization and hence the determination of the underlying probability density functions. Totally ten datasets, including four normally-distributed synthetic ones, three normally-distributed ones on the elasticity of rocks obtained by statistical nanoindentation, and three lognormally-distributed ones on the particle size distributions of flocculated clay suspensions, were used to illustrate the BSI's concepts and algorithms. While results from the synthetic datasets prove the method's accuracy and effectiveness, analyses of other real datasets from materials characterization and measurement further demonstrate its rationale, performance, and applicability to practical problems. The BSI method also enables determination of the number of modes via the comparative evaluation of the errors returned from different trial bin sizes. The accuracy and performance of the BSI method are further compared with other widely used binning methods, and the former yields the highest BSI and smallest normalized standard errors. This new method particularly penalizes the overfitting that tends to yield too many pseudo-modes via normalizing the errors by the number of modes hidden in the datasets, and also eliminates the difficulty in specifying criteria for acceptable values of the fitting errors. The advantages and disadvantages of the new method are also discussed.

Definition of the Region of Interest for the Assessment of Alveolar Bone Repair Using Micro-computed Tomography

The objective of this study was to evaluate the influence of the extraction socket (distal or lingual root) and the type of region of interest (ROI) definition (manual or predefined) on the assessment of alveolar repair following tooth extraction using micro-computed tomography (micro-CT). The software package used for scanning, reconstruction, reorientation, and analysis of images (NRecon®, DataViewer®, CT-Analyzer®) was acquired through Bruker <  https://www.bruker.com  > . The sample comprised the micro-CT volumes of seven Wistar rat mandibles, in which the right first molar was extracted. The reconstructed images were analyzed using the extraction sockets, i.e., the distal and intermediate lingual root and the method of ROI definition: manual (MA), central round (CR), and peripheral round (PR). The bone volume fraction (BV/TV) values obtained were analyzed by two-way ANOVA with Tukey's post hoc test (α = 5%). The distal extraction socket resulted in significantly lower BV/TV values than the intermediate lingual socket for MA (P = 0.001), CR (P < 0.001), and PR (P < 0.001). Regarding the ROI, when evaluating the distal extraction socket, the BV/TV was significantly higher (P < 0.001) for MA than for CR and PR, with a lower BV/TV for CR. However, no significant difference was observed for MA (P = 0.855), CR (P = 0.769), or PR (P = 0.453) in the intermediate lingual extraction socket. The bone neoformation outcome (BV/TV) for alveolar bone repair after tooth extraction is significantly influenced by the ROI and the extraction socket. Using the predefined method with a standardized ROI in the central region of the distal extraction socket resulted in the assessment of bone volume, demonstrating the most critical region of the bone neoformation process.