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Updated: Jun 28, 2026
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
Published on: August 5, 2021
Geometric Fidelity of Magnetic Resonance Imaging and Computed Tomography-Derived Virtual 3D Models of Porcine Cadaver
Purpose:
The workflow for virtual surgical planning (VSP) and the application of CAD/CAM (computer-aided design/computer-aided manufacturing) procedures are mainly based on computed tomography (CT) derived DICOM data sets. Alternatively, this study aims to preclinically illuminate the feasibility of a magnetic resonance imaging (MRI) based workflow and the impact of artificial intelligence (AI) based segmentation on the required fidelity on basic 3D geometry acquisition.
Materials And Methods:
Porcine cadaver mandibles were imaged with CT and a T1-weighted MRI sequence. The resulting DICOM data sets were segmented conventionally (Mimics Medical 17.0, Materialize; Belgium) and with AI-based segmentation software (ImFusion Labels and Suite, Version 2.19.2, ImFusion; Germany). The four standard tessellation language (STL) files were superimposed with a corresponding reference model derived from an optic scan (Artec Space Spider, Artec 3D; Luxembourg) and the following parameters were analysed: Hausdorff distance (HD), mean surface distance (MSD), root mean square distance (RMSD), time.
Results:
In comparison to the reference model, all four parameters were significantly (P 0.001) better for the CT imaging and the AI-based segmentation. MRI-derived AI-based segmentation reached the fidelity of CT imaging data sets and conventional segmentation (HD, MSD, and RMSD each P >0.05).
Conclusion:
The use of AI-based segmentation software proved to be useful and feasible for MRI-derived data sets, and generated the desired 3D geometry more quickly while maintaining the necessary quality. Nevertheless, the results for the CT were still better and remain yet the standard.
