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Accuracy quantification of a deformable image registration tool applied in a clinical setting

机译:在临床环境中应用的可变形图像配准工具的精度量化

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摘要

The purpose of this study was to test the accuracy of a commercially available deformable image registration tool in a clinical situation. In addition, to demonstrate a method to evaluate the resulting transformation of such a tool to a reference defined by multiple experts. For 16 patients (seven head and neck, four thoracic, five abdominal), 30‐50 anatomical landmarks were defined on recognizable spots of a planning CT and a corresponding fraction CT. A commercially available deformable image registration tool, Velocity AI, was used to align all fraction CTs with the respective planning CTs. The registration accuracy was quantified by means of the target registration error in respect to expert‐defined landmarks, considering the interobserver variation of five observers. The interobserver uncertainty of the landmark definition in our data sets is found to be 1.2±1.1mm. In general the deformable image registration tool decreases the extent of observable misalignments from 4‐8 mm to 1‐4 mm for nearly 50% of the landmarks (to 77% in sum). Only small differences are observed in the alignment quality of scans with different tumor location. Smallest residual deviations were achieved in scans of the head and neck region (79%,4mm) and the thoracic cases (79%,4mm), followed by the abdominal cases (59%,4mm). No difference is observed in the alignment quality of different tissue types (bony vs. soft tissue). The investigated commercially available deformable image registration tool is capable of reducing a mean target registration error to a level that is clinically acceptable for the evaluation of retreatment plans and replanning in case of gross tumor change during treatment. Yet, since the alignment quality needs to be improved further, the individual result of the deformable image registration tool has still to be judged by the physician prior to application.PACS numbers: 87.57.nj, 87.57.N‐, 87.55.‐x
机译:这项研究的目的是在临床情况下测试市售可变形图像配准工具的准确性。另外,演示了一种评估将这种工具转换为由多个专家定义的参考的方法。对于16例患者(七个头颈部,四个胸腔,五个腹部),在计划CT和相应分数CT的可识别点上定义了30-50个解剖学标志。使用可商购的可变形图像配准工具Velocity AI将所有分数CT与各自的计划CT对齐。考虑到五个观察者之间的观察者差异,通过专家定义的地标的目标注册误差来量化注册准确性。我们的数据集中地标定义的观察者间不确定性被发现为 1.2 ± 1.1 mm 。通常,可变形图像配准工具可将近50%的界标的可观察到的未对准范围从4-8 mm减少到1-4 mm(总计为77%)。在具有不同肿瘤位置的扫描的对准质量中仅观察到很小的差异。在头部和颈部区域的扫描中,残留偏差最小( 79 4 mm )和胸部病例( 79 4 mm ),其次是腹部病例( 59 4 mm )。在不同组织类型(骨组织与软组织)的对齐质量上未观察到差异。经研究的可商购的可变形图像配准工具能够将平均目标配准误差降低到临床上可接受的水平,以评估再治疗计划和治疗期间肿瘤发生总体变化的再计划。但是,由于需要进一步提高对齐质量,因此在使用前仍需医生判断可变形图像配准工具的个别结果.PACS编号:87.57.nj,87.57.N‐,87.55.‐x

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