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Updated: Jun 1, 2026
A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth
Published on: April 8, 2020
Evaluation of CBCT reconstructed tooth models at different thresholds and voxels and their accuracy in fusion with
Yusong Zhang1, Yixin Liu1, Tianqi Liu2
1Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China.
Background:
This study aims to evaluate the impact of different thresholds and voxel sizes on the accuracy of Cone-beam computed tomography (CBCT) tooth reconstruction and to assess the accuracy of fused CBCT and intraoral scanning (IOS) tooth models using curvature continuity algorithms under varying thresholds and voxel conditions.
Methods:
Thirty-two isolated teeth were digitized using IOS and CBCT at two voxel sizes and five threshold settings. Crown-root fusion was performed using a curvature continuity algorithm. Volume, surface area, and crown width of tooth models were compared to laser scanning models, and RMS error was measured. Data were analyzed using Wilcoxon signed-rank test, paired t-test, and one-way ANOVA.
Results:
Volume amplification errors of CBCT with 0.15 mm and 0.3 mm voxels ranged from 1.22 to 19.07%, surface area errors from 0.18 to 7.78%, crown linearity errors ranged from 2.47 to 7.69%, root linearity errors ranged from - 1.02 to 2.26% and RMS from 0.0691 mm to 0.2408 mm. Crown-root fusion of IOS and CBCT data reduced volume error to -0.90-5.10%, surface area error to -0.66-4.15%, and RMS to 0.0359 mm to 0.0945 mm.
Conclusions:
Voxel size and threshold settings significantly affect the accuracy of CBCT reconstruction and crown-root fusion. Smaller voxel sizes yield higher reconstruction precision, and different voxel sizes and tooth regions correspond to distinct optimal segmentation thresholds. The validated semi-automated crown-root fusion algorithm significantly enhances overall model accuracy, offering new possibilities for clinical applications.
