Zhang Yusong, Liu Yixin, Liu Tianqi, Zhang Jiahao, Lin Peiying, Liu Dongxu
Department 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.
Department of Sociology, School of Social Sciences, University of California, Irvine, USA.
BMC Oral Health. 2024 Dec 30;24(1):1571. doi: 10.1186/s12903-024-05395-z.
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.
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.
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.
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.
本研究旨在评估不同阈值和体素大小对锥束计算机断层扫描（CBCT）牙齿重建准确性的影响，并评估在不同阈值和体素条件下，使用曲率连续性算法融合CBCT和口腔内扫描（IOS）牙齿模型的准确性。
使用IOS和CBCT在两种体素大小和五种阈值设置下对32颗离体牙进行数字化处理。使用曲率连续性算法进行冠根融合。将牙齿模型的体积、表面积和冠宽度与激光扫描模型进行比较，并测量均方根误差（RMS）。使用Wilcoxon符号秩检验、配对t检验和单因素方差分析对数据进行分析。
体素大小为0.15毫米和0.3毫米的CBCT的体积放大误差范围为1.22%至19.07%，表面积误差范围为0.18%至7.78%，冠线性误差范围为2.47%至7.69%，根线性误差范围为-1.02%至2.26%，RMS范围为0.0691毫米至0.2408毫米。IOS和CBCT数据的冠根融合将体积误差降低至-0.90%至5.10%，表面积误差降低至-0.66%至4.15%，RMS降低至0.0359毫米至0.0945毫米。
体素大小和阈值设置显著影响CBCT重建和冠根融合的准确性。较小的体素大小产生更高的重建精度，不同的体素大小和牙齿区域对应不同的最佳分割阈值。经过验证的半自动冠根融合算法显著提高了整体模型准确性，为临床应用提供了新的可能性。
