Improving the prediction of the trabecular bone microarchitectural parameters using dental cone-beam computed tomography

BACKGROUND: In this study, we explored how various preprocessing approaches can be employed to enhance the capability of dental CBCT to accurately estimate trabecular bone microarchitectural parameters. METHODS: In total, 30 bovine vertebrae cancellous bone specimens were used for in study. Voxel resolution 18-μm micro-computed tomography (micro-CT) and 100-μm dental CBCT were used to scan each specimen. Micro-CT images were used to calculate trabecular bone microarchitectural parameters; the results were set as the gold standard. Subsequently, before the dental CBCT images were converted into binary images to calculate trabecular bone microarchitectural parameters, three preprocessing approaches were used to process the dental CBCT images. For Group 1, no preprocessing approach was applied. For Group 2, images were sharpened and despeckable noises were removed. For Group 3, the function of local thresholding was added to Group 2 to form Group 3. For Group 4, the air pixels was removed from Group 3 to form Group 4. Subsequently, all images were imported into a software package to estimate trabecular bone microarchitectural parameters (bone volume fraction (BV/TV), trabecular thickness (TbTh), trabecular number (TbN), and trabecular separation (TbSp)). Finally, a paired t-test and a Pearson correlation test were performed to compare the capability of micro-CT with the capability of dental CBCT for estimating trabecular bone microarchitectural parameters. RESULTS: Regardless of whether dental CBCT images underwent image preprocessing (Groups 1 to 4), the four trabecular bone microarchitectural parameters measured using dental CBCT images were significantly different from those measured using micro-CT images. However, after three image preprocessing approaches were applied to the dental CBCT images (Group 4), the BV/TV obtained using dental CBCT was highly positively correlated with that obtained using micro-CT (r = 0.87, p < 0.001); the correlation coefficient was greater than that of Group 1 (r = −0.15, p = 0.412), Group 2 (r = 0.16, p = 0.386), and Group 3 (r = 0.47, p = 0.006). After dental CBCT images underwent image preprocessing, the efficacy of using dental CBCT for estimating TbN and TbSp was enhanced. CONCLUSIONS: Image preprocessing approaches can be used to enhance the efficacy of using dental CBCT for predicting trabecular bone microarchitectural parameters.