Geometric Fidelity of Magnetic Resonance Imaging and Computed Tomography-Derived Virtual 3D Models of Porcine Cadaver Mandibles: Conventional Versus Artificial Intelligence-Based Segmentation

TL;DR

This study compares MRI and CT imaging for creating 3D models of pig jaws, showing that AI-based segmentation can match CT quality and speed.

Contribution

Demonstrates that AI-based segmentation of MRI data can achieve CT-level geometric fidelity for 3D modeling in surgical planning.

Findings

AI-based segmentation of MRI data achieved similar geometric accuracy to CT data.

AI-based methods were faster than conventional segmentation while maintaining quality.

CT imaging still outperformed MRI in all measured parameters but remains the standard.

Abstract

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.

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.

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).

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.

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