Department of Prosthodontics, King's College London Dental Institute, Floor 25, Tower Wing, Guy's Hospital, London SE19RT, UK.
Department of Medical Technologies, University of Siena, Siena, Italy; Department of Restorative Dentistry, Leeds School of Dentistry, Clarendon Way, Leeds LS2 9LU, UK.
Dent Mater. 2019 Mar;35(3):495-500. doi: 10.1016/j.dental.2019.01.012. Epub 2019 Jan 23.
Alignment procedures have yet to be standardised and may influence the measurement outcome. This investigation assessed the accuracy of commonly used alignment techniques and their impact on measurement metrics.
Datasets of 10 natural molar teeth were created with a structured-light model-scanner (Rexcan DS2, Europac 3D, Crewe). A 300μm depth layer was then digitally removed from the occlusal surface creating a defect of known size. The datasets were duplicated, randomly repositioned and re-alignment attempted using a "best-fit" alignment, landmark-based alignment or reference alignment in Geomagic Control (3D Systems, Darmstadt, Germany). The re-alignment accuracy was mathematically assessed using the mean angular and translation differences between the original alignment and the re-aligned datasets. The effect of the re-alignment on conventional measurement metrics was calculated by analysing differences between the known defect size and defect size after re-alignment. Data were analysed in SPSS v24(ANOVA, post hoc Games Howell test, p<0.05).
The mean translation error (SD) was 139μm (42) using landmark alignment, 130μm (26) for best-fit and 22μm (9) for reference alignment (p<0.001). The mean angular error (SD) between the datasets was 2.52 (1.18) degrees for landmark alignment, 0.56 (0.38) degrees for best-fit alignment and 0.26 (0.12) degrees for reference alignment (p<0.001). Using a reference alignment statistically reduced the mean profilometric change, volume change and percentage of surface change errors (p<0.001).
Reference alignment produced significantly lower alignment errors and truer measurements. Best-fit and landmark-based alignment algorithms significantly underestimated the size of the defect. Challenges remain in identifying reference surfaces in a robust, clinically relevant method.
对齐程序尚未标准化，可能会影响测量结果。本研究评估了常用对齐技术的准确性及其对测量指标的影响。
使用结构光模型扫描仪（Rexcan DS2，Europac 3D，Crewe）创建 10 颗天然磨牙的数据集。然后从咬合面数字去除 300μm 深度层，形成已知大小的缺陷。数据集被复制，使用“最佳拟合”对齐、基于地标对齐或在 Geomagic Control（3D Systems，Darmstadt，德国）中的参考对齐重新定位并尝试重新对齐。使用原始对齐和重新对齐的数据集之间的平均角度和翻译差异来数学评估重新对齐的准确性。通过分析重新对齐后的已知缺陷尺寸和缺陷尺寸之间的差异来计算重新对齐对常规测量指标的影响。数据使用 SPSS v24 进行分析（ANOVA，事后 Games Howell 检验，p<0.05）。
使用地标对齐的平均平移误差（SD）为 139μm（42），最佳拟合为 130μm（26），参考对齐为 22μm（9）（p<0.001）。数据集之间的平均角度误差（SD）为地标对齐为 2.52（1.18）度，最佳拟合对齐为 0.56（0.38）度，参考对齐为 0.26（0.12）度（p<0.001）。使用参考对齐可显著降低平均轮廓变化、体积变化和表面变化百分比误差（p<0.001）。
参考对齐产生的对齐误差和测量结果更小。最佳拟合和基于地标对齐算法显著低估了缺陷的大小。在以稳健、临床相关的方法识别参考表面方面仍然存在挑战。
