Methodological Evaluation of Micro-CT Analytical Parameters for Quantifying Bone Loss in a Murine Maxillary...

Background: Peri-implantitis is a prevalent inflammatory condition leading to progressive bone loss around dental implants. In vivo studies widely use murine peri-implantitis models. Micro-computed tomography (micro-CT) is the gold standard method used to assess peri-implant bone changes in those models. However, no standardized protocol exists for image analysis, limiting comparability across studies. Objective: This study aimed to evaluate the effect of different micro-CT analysis parameters on bone measurements in a ligature-induced peri-implantitis mouse model. Methods: Seventeen C57BL/6J mice were divided into peri-implantitis (n = 8) and healthy (n = 9) groups. Following extraction and implant placement in the maxillary first molar region, peri-implantitis was induced via silk ligature placement. Micro-CT scans were analyzed by varying the volume of interest (VOI) size (2-thread vs. 5-thread spans), threshold values for bone and implant visualization, and measurement dimensionality (2D linear vs. 3D volumetric). Statistical significance (p < 0.05) was determined using unpaired t-tests or Mann-Whitney U tests, following Shapiro-Wilk normality assessment. Results: The 2-thread VOI yielded a highly significant difference in bone volume fraction (BV/TV) between groups (68.86 ± 4.97% vs. 50 ± 4.32%, p < 0.0001, Cohen's d = 4.05), while the 5-thread VOI showed no significant difference. Thresholds of 60 and 200 were utilized for bone and implant segmentation, respectively, selected based on high inter-examiner reliability (Cohen's kappa = 0.844 and 0.825, respectively). Two-dimensional analysis confirmed greater mesio-distal loss in the peri-implantitis group. Conclusions: A 2-thread coronal VOI combined with optimized thresholds provides a promising analytical approach for evaluating localized peri-implant bone changes in this exploratory model. While this approach demonstrated pronounced responsiveness within our dataset, further external validation across different imaging systems and operators is required to establish its role as a universal standard.