Assessment of metal artifacts in three-dimensional dental surface models derived by cone-beam computed tomography.

OBJECTIVE: The aim of this study was to assess artifacts induced by metallic restorations in three-dimensional (3D) dental surface models derived by cone-beam computed tomography (CBCT). METHODS: Fifteen specimens, each with four extracted human premolars and molars embedded in a plaster block, were scanned by CBCT before and after the cavitated second premolars were restored with dental amalgam. Five consecutive surface models of each specimen were created according to increasing restoration size: no restoration (control) and small occlusal, large occlusal, disto-occlusal, and mesio-occluso-distal restorations. After registering each restored model with the control model, maximum linear discrepancy, area, and intensity of the artifacts were measured and compared. RESULTS: Artifacts developed mostly on the buccal and lingual surfaces. They occurred not only on the second premolar but also on the first premolar and first molar. The parametric values increased significantly with increasing restoration size. CONCLUSIONS: Metallic restorations induce considerable artifacts in 3D dental surface models. Artifact reduction should be taken into consideration for a proper diagnosis and treatment planning when using 3D surface model derived by CBCT in dentofacial deformity patients.

Registration accuracy in the integration of laser-scanned dental images into maxillofacial cone-beam computed tomography images.

INTRODUCTION: A precision 3-dimensional (3D) head model can be fabricated by integrating a digital dental model into a maxillofacial 3D image. The integration requires accurate registration of 2 image modalities. The aims of this study were to determine the registration errors for implementation of laser-scanned dental images into cone-beam computed tomography (CBCT) scan data and to examine the influence of the registration area on the accuracy of registration. METHODS: The CBCT scans were obtained from 30 adults, and the maxillofacial 3D images were reconstructed. Maxillary and mandibular dental casts were taken from the same subjects and scanned with a 3D laser scanner. The laser-scanned maxillary and mandibular dentition images were incorporated into the CBCT images of each arch in 3 ways according to the registration area: only the buccal surfaces, only the lingual surfaces, and both the buccal and lingual surfaces. Surface-based registration was performed by using an iterative closest point algorithm, and its errors were evaluated by measuring the 3D Euclidean distances between the surface points on the 2 images. RESULTS: The registration errors ranged from 0.27 to 0.33 mm. The mandibular arch did not show significant differences in registration errors according to the selected area for the registration. The maxillary arch, however, showed significant differences according to the registration area. When the lingual surfaces only were used for registration, the errors were greater than for the other 2 methods. The errors were least when both the buccal and lingual surfaces were used for registration. CONCLUSIONS: The results of this study indicate that accuracy in the integration of laser-scanned dental images into the maxillofacial CBCT images increases when a broad area is used for registration.