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Statement of Problem: Fabrication of conventional facial prostheses is a labor-intensive process which traditionally requires an impression of the facial defect and surrounding tissues. Inaccuracies occur during the facial moulage because of soft-tissue compression, the patient's reflex movements, or the lack of support for the impression material. A variety of 3D imaging techniques have been introduced during the production of facial prostheses. However, the accuracy of the different imaging techniques has not been evaluated sufficiently in this clinical context., Purpose: The purpose of this in vitro study was to compare the difference in accuracy of capturing oncology facial defects with multimodal image fusion and laser scanning against a cone beam computed tomography (CBCT) reference scan., Material and Methods: Ten gypsum casts of oncology facial defects were acquired. To produce reference models, a 3D volumetric scan was obtained using a CBCT scanner and converted into surface data using open-source medical segmentation software. This model was cropped to produce a CBCT mask using an open-source system for editing meshes. The multimodal image fusion model was created using stereophotogrammetry to capture the external facial features and a custom optical structured light scanner to record the defect. The gypsum casts were also scanned using a commercial 3D laser scanner to create the laser-scanned model. Analysis of the best fit of each experimental model to the CBCT mask was performed in MeshLab. The unsigned mean distance was used to measure the absolute deviation of each model from the CBCT mask. A paired-samples t test was conducted to compare the mean global deviation of the 2 imaging modalities from the CBCT masks (α=.05)., Results: A statistically significant difference was found in the mean global deviation between the multimodal imaging model (220 ±50 μm) and the laser-scanned model (170 ±70 μm); (t(9)=2.56, P=.031). The color error maps illustrated that the greatest error was located at sites distant to the prosthesis margins., Conclusions: The laser-scanned models were more accurate; however, the mean difference of 50 μm is unlikely to be clinically significant. The laser scanner had limited viewing angles and a longer scan time which may limit its transferability to maxillofacial practice., (Copyright © 2019 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.)

Aim: Assess the feasibility, validity and precision of multimodal image fusion to capture oncology facial defects based on plaster casts., Methods: Ten casts of oncology facial defects were acquired. To create gold standard models, a 3D volumetric scan of each cast was obtained with a cone beam computed tomography (CBCT) scanner (NewTomVG). This was converted into surface data using open-source medical segmentation software and cropped to produce a CBCT mask using an open-source system for editing meshes. For the experimental model, the external facial features were captured using stereophotogrammetry (DI4D) and the defect was recorded with a custom optical structured light scanner. The two meshes were aligned, merged and resurfaced using MeshLab to produce a fused model. Analysis was performed in MeshLab on the best fit of the fused model to the CBCT mask. The unsigned mean distance was used to measure the absolute deviation of each model from the CBCT mask. To assess the precision of the technique, the process of producing the fused model was repeated to create five models each for the casts representing the best, middle and worst results., Results: Global mean deviation was 0.22 mm (standard deviation 0.05 mm). The precision of the method appeared to be acceptable although there was variability in the location of the error for the worst cast., Conclusion: This method for merging two independent scans to produce a fused model shows strong potential as an accurate and repeatable method of capturing facial defects. Further research is required to explore its clinical use., (Copyright © 2017 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.)