Geometric Reproducibility of Three-Dimensional Oral Implant Planning Based on Magnetic Resonance Imaging and Cone-Beam Computed Tomography.
Franz Sebastian SchwindlingSophia BoehmChristopher HerpelDorothea KronsteinerLorenz VogelAlexander JuerchottSabine HeilandMartin BendszusPeter RammelsbergTim HilgenfeldPublished in: Journal of clinical medicine (2021)
This study aimed to investigate the geometric reproducibility of three-dimensional (3D) implant planning based on magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT). Four raters used a backward-planning approach based on CBCT imaging and standard software to position 41 implants in 27 patients. Implant planning was repeated, and the first and second plans were analyzed for geometric differences regarding implant tip, entry-level, and axis. The procedure was then repeated for MRI data of the same patients. Thus, 656 implant plans were available for analysis of intra-rater reproducibility. For both imaging modalities, the second-round 3D implant plans were re-evaluated regarding inter-rater reproducibility. Differences between the modalities were analyzed using paired t-tests. Intra- and inter-rater reproducibility were higher for CBCT than for MRI. Regarding intra-rater deviations, mean values for MRI were 1.7 ± 1.1 mm/1.5 ± 1.1 mm/5.5 ± 4.2° at implant tip/entry-level/axis. For CBCT, corresponding values were 1.3 ± 0.8 mm/1 ± 0.6 mm/4.5 ± 3.1°. Inter-rater comparisons revealed mean values of 2.2 ± 1.3 mm/1.7 ± 1 mm/7.5 ± 4.9° for MRI, and 1.7 ± 1 mm/1.2 ± 0.7 mm/6 ± 3.7° for CBCT. CBCT-based implant planning was more reproducible than MRI. Nevertheless, more research is needed to increase planning reproducibility-for both modalities-thereby standardizing 3D implant planning.
Keyphrases
- cone beam computed tomography
- magnetic resonance imaging
- contrast enhanced
- soft tissue
- end stage renal disease
- diffusion weighted imaging
- computed tomography
- chronic kidney disease
- ejection fraction
- image quality
- newly diagnosed
- high resolution
- magnetic resonance
- prognostic factors
- peritoneal dialysis
- machine learning
- photodynamic therapy
- big data
- deep learning
- patient reported