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Surface Registration Accuracy of Clinically Obtained Intraoral Optical Scans with Manually Threshold Segmented CBCT Data

机译:具有手动阈值分割CBCT数据的临床获得的口腔内光学扫描的表面配准精度

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Combining CBCT imaging and intraoral scans with an intention to prepare comprehensive treatment plans is common in clinical practice. Segmentation and superimposition of models is indispensable when planning complicated prosthetic reconstructions. The authors of this publication evaluated registration discrepancies of virtual dental arch models obtained by means of CBCT imaging at different segmentation thresholds with intraoral dental scans. For comparisons, intraoral digital scans and volumetric CBCT scans of the upper and lower jaw were used in randomly selected patients. The mean distance, Gaussian mean and standard deviation from the Gaussian mean as registration inconsistencies between the combined models were measured and subjected to a statistical analysis. The results showed that the registration of superimposed models may be affected by errors of up to 300 microns in the case of full dental arches. The statistical analysis proved that there was no correlation between the segmentation threshold and the quantitative variables studied (e.g. mean distance, Gaussian mean and standard deviation from the Gaussian mean). The results of this study indicate that CBCT data and full arch optical scans can be superimposed and successfully applied under clinical conditions within the accepted error.
机译:在临床实践中,将CBCT成像和口内扫描相结合以准备全面的治疗计划是很常见的。当计划复杂的假体重建时,模型的分割和叠加是必不可少的。该出版物的作者评估了通过口腔内扫描在不同分割阈值下通过CBCT成像获得的虚拟牙弓模型的配准差异。为了进行比较,在随机选择的患者中使用了上颌和下颌的口内数字扫描和体积CBCT扫描。测量组合模型之间的配准不一致情况下的平均距离,高斯平均数和与高斯平均数的标准偏差,并进行统计分析。结果表明,在完全牙弓的情况下,叠加模型的注册可能受到最大300微米的误差的影响。统计分析证明,分割阈值与所研究的定量变量(例如,平均距离,高斯均值和与高斯均值的标准偏差)之间没有关联。这项研究的结果表明,可以将CBCT数据和全弓形光学扫描叠加并在临床条件下在可接受的误差范围内成功应用。

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