Accuracy and reliability of stitched cone-beam computed tomography images.
- Author:
Nicholas EGBERT
1
;
David R CAGNA
;
Swati AHUJA
;
Russell A WICKS
Author Information
- Publication Type:Original Article
- Keywords: Cone-Beam Computed Tomography; Computer-Aided Design; Dental Implants
- MeSH: Cadaver; Computer-Aided Design; Cone-Beam Computed Tomography*; Dataset; Dental Implants; Denture, Complete; Gutta-Percha; Mandible; Stents
- From:Imaging Science in Dentistry 2015;45(1):41-47
- CountryRepublic of Korea
- Language:English
- Abstract: PURPOSE: This study was performed to evaluate the linear distance accuracy and reliability of stitched small field of view (FOV) cone-beam computed tomography (CBCT) reconstructed images for the fabrication of implant surgical guides. MATERIALS AND METHODS: Three gutta percha points were fixed on the inferior border of a cadaveric mandible to serve as control reference points. Ten additional gutta percha points, representing fiduciary markers, were scattered on the buccal and lingual cortices at the level of the proposed complete denture flange. A digital caliper was used to measure the distance between the reference points and fiduciary markers, which represented the anatomic linear dimension. The mandible was scanned using small FOV CBCT, and the images were then reconstructed and stitched using the manufacturer's imaging software. The same measurements were then taken with the CBCT software. RESULTS: The anatomic linear dimension measurements and stitched small FOV CBCT measurements were statistically evaluated for linear accuracy. The mean difference between the anatomic linear dimension measurements and the stitched small FOV CBCT measurements was found to be 0.34 mm with a 95% confidence interval of +0.24 - +0.44 mm and a mean standard deviation of 0.30 mm. The difference between the control and the stitched small FOV CBCT measurements was insignificant within the parameters defined by this study. CONCLUSION: The proven accuracy of stitched small FOV CBCT data sets may allow image-guided fabrication of implant surgical stents from such data sets.
