Virtual anatomic phantoms offer precise voxel mapping of the variation of anatomy with ground truth deformation vector fields (DVFs). Dice similarity coefficient (DSC) and mean distance to agreement (MDA) are the standard metrics for evaluating geometric contour congruence when testing deformable registration (DIR) algorithms. A HN virtual patient phantom data set was used for a kVCT‐kVCT automatic propagation contour validation study employing the Accuray DIR algorithm. Furthermore, since TomoTherapy uses MVCT images of the relevant anatomy for adaptive monitoring, the kVCT image data set quality was transformed to an MVCT image data set quality to study intermodal kVCT‐MVCT DIR accuracy. The results of the study indicate that the Accuray DIR algorithm can be expected to autopropagate HN contours adequately, on average, within tolerances recommended by TG‐132 (DSC 0.8‐0.9, MDA within voxel width). However, contours critical to dosimetric planning should always be visually proofed for accuracy. Using standard reconstruction MVCT image quality causes slightly less, but acceptable, agreement with ground truth contours.

中文翻译：

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使用基准虚拟HN幻象数据集验证可变形图像配准轮廓传播的准确性

虚拟解剖体模提供了具有地面真实变形矢量场（DVF）的解剖结构变化的精确体素映射。骰子相似性系数（DSC）和平均一致距离（MDA）是在测试可变形配准（DIR）算法时评估几何轮廓一致性的标准度量。HN虚拟患者体模数据集用于采用Accuray DIR算法的kVCT-kVCT自动传播轮廓验证研究。此外，由于TomoTherapy使用相关解剖结构的MVCT图像进行自适应监测，因此将kVCT图像数据集质量转换为MVCT图像数据集质量，以研究联运kVCT-MVCT DIR准确性。研究结果表明，平均而言，可以预期Accuray DIR算法能够适当地自动传播HN轮廓，在TG-132建议的公差范围内（DSC 0.8-0.9，MDA在体素宽度内）。但是，对于剂量规划至关重要的轮廓，应始终在视觉上经过验证以确保准确性。使用标准重建MVCT图像质量导致与地面真实轮廓的一致性略低，但可以接受。