Image Fusion in Craniofacial Virtual Reality Modeling Based on CT and 3dMD Photogrammetry

Purpose: The aim of this study was to demonstrate the feasibility of building a craniofacial virtual reality model by image fusion of 3-dimensional (3D) CT models and 3dMD stereophotogrammetric facial surface. Methods: A CT scan and stereophotography were performed. The 3D CT models were reconstructed by Materialise Mimics software, and the stereophotogrammetric facial surface was reconstructed by 3dMD patient software. All 3D CT models were exported as Stereo Lithography file format, and the 3dMD model was exported as Virtual Reality Modeling Language file format. Image registration and fusion were performed in Mimics software. Genetic algorithm was used for precise image fusion alignment with minimum error. The 3D CT models and the 3dMD stereophotogrammetric facial surface were finally merged into a single file and displayed using Deep Exploration software. Errors between the CT soft tissue model and 3dMD facial surface were also analyzed. Results: Virtual model based on CT-3dMD image fusion clearly showed the photorealistic face and bone structures. Image registration errors in virtual face are mainly located in bilateral cheeks and eyeballs, and the errors are more than 1.5 mm. However, the image fusion of whole point cloud sets of CT and 3dMD is acceptable with a minimum error that is less than 1 mm. Conclusions: The ease of use and high reliability of CT-3dMD image fusion allows the 3D virtual head to be an accurate, realistic, and widespread tool, and has a great benefit to virtual face model.