An in-vitro study to assess the feasibility, validity and precision of capturing oncology facial defects with multimodal image fusion.

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.