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Exploring the clinical validity of predicted TRE in navigation

机译:探索导航中预测TRE的临床有效性

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In a detailed laboratory investigation we performed a series of experiments in order to assess the validity of the widely used TRE concept to predict the application accuracy. On base of lmm CT scan a plastic skull, a cadaver head and a volunteer were registered to an in house navigation system. We stored the position data of an optical camera (NDI Polaris) for registration with pre-defined CT coordinates. For every specimen we choose 3, 5, 7 and 9 registration and 10 evaluation points, respectively, performing 10 registrations. The data were evaluated both with the Arun and the Horn approaches. The vectorial difference between actual and predefined position in the CT data set was stored and evaluated for FRE and TRE. Evaluation and visualization was implemented in Matlab. The data were analyzed, specifically for normal distribution, with MS Excel and SPSS Version 15.0.For the plastic skull and the anatomic specimen submillimetric application accuracy was found experimentally and confirmed by the calculated TRE. Since for the volunteer no Titanium screws were implanted anatomic landmarks had to be used for registration and evaluation; an application accuracy in the low millimeter regime was found in all approaches. However, the detailed statistical analysis of the data revealed that the model predictions and the actual measurements do not exhibit a strong statistical correlation (p < 0.05). These data suggest that the TRE predictions are too optimistic and should be used with caution intraoperatively.
机译:在详细的实验室研究中,我们进行了一系列实验,以评估广泛使用的TRE概念预测应用准确性的有效性。在1mm CT扫描的基础上,将一个塑料头骨,一个尸体头和一个志愿者注册到了室内导航系统中。我们存储了光学摄像机(NDI Polaris)的位置数据,以便与预定义的CT坐标进行配准。对于每个样本,我们分别选择3、5、7和9个配准和10个评估点,进行10个配准。使用Arun和Horn方法对数据进行评估。存储了CT数据集中实际位置和预定义位置之间的矢量差,并针对FRE和TRE进行了评估。在Matlab中实现了评估和可视化。使用MS Excel和SPSS 15.0版对数据进行了分析,尤其是正态分布的数据。 对于塑料头骨和解剖标本,通过实验发现了亚毫米级的应用精度,并通过计算的TRE进行了确认。由于对于志愿者来说,没有植入钛螺钉,所以必须使用解剖学界标进行注册和评估。在所有方法中都发现了低毫米范围的应用精度。但是,对数据进行的详细统计分析表明,模型预测和实际测量值之间没有显示出很强的统计相关性(p <0.05)。这些数据表明,TRE的预测过于乐观,术中应谨慎使用。

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