PURPOSE.The aim of this study was to examine the importance of the defect-free scanning of a scan body by assessing the accuracy of virtual implant positioning in computer-aided design (CAD) software when the scan body image is improperly scanned. MATERIALS AND METHODS. A scan body was digitized in a dentiform model using an intraoral scanner, and scanned images with differing levels of image deficiency were generated: 5%, 10%, and 15% deficiency in the flat or rounded area. Using a best-fit image matching algorithm on each of the deficient scan body images, corresponding virtual implants were created. The accuracy of the implant position was evaluated by comparing the linear and angular discrepancies between the actual and virtual positions of the implant. Kruskal-Wallis tests and Mann-Whitney U tests with Bonferroni correction were used to determine the statistical differences among the seven scanned image deficiency groups (α=.05). RESULTS. In general, the linear and angular discrepancies of the implant position in the software increased as the deficiency of the scan body images increased. A 15% scan body image deficiency generated larger discrepancies than deficiency of 5% and 10%. The difference of scan defect position, flat or rounded area, did not affect the accuracy of virtual implant orientation at 5% and 10% deficiency level, but did affect the accuracy at 15% deficiency level. CONCLUSION. Deficiencies in the scanned images of a scan body can decrease the accuracy of the implant positioning in CAD software when the defect is large, thus leading to the incorrect fabrication of implant prostheses. [ J Adv

In cone-beam computed tomography (CBCT), the minimum threshold of the gray value of segmentation is set to convert the CBCT images to the 3D mesh reconstruction model. This study aimed to assess the accuracy of image registration of optical scans to 3D CBCT reconstructions created by different thresholds of grey values of segmentation in partial edentulous jaw conditions. CBCT of a dentate jaw was reconstructed to 3D mesh models using three different thresholds of gray value (−500, 500, and 1500), and three partially edentulous models with different numbers of remaining teeth (4, 8, and 12) were made from each 3D reconstruction model. To merge CBCT and optical scan data, optical scan images were registered to respective 3D reconstruction CBCT images using a point-based best-fit algorithm. The accuracy of image registration was assessed by measuring the positional deviation between the matched 3D images. The Kruskal–Wallis test and a post hoc Mann–Whitney U test with Bonferroni correction were used to compare the results between groups (α = 0.05). The correlations between the experimental factors were calculated using the two-way analysis of variance test. The positional deviations were lowest with the threshold of 500, followed by the threshold of 1500, and then −500. A significant interaction was found between the threshold of gray values and the number of remaining teeth on the registration accuracy. The most significant deviation was observed in the arch model with four teeth reconstructed with a gray-value threshold of −500. The threshold for the gray value of CBCT segmentation affects the accuracy of image registration of optical scans to the 3D reconstruction model of CBCT. The appropriate gray value that can visualize the anatomical structure should be set, especially when few teeth remain in the dental arch.

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