Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA.
Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY, USA.
J Appl Clin Med Phys. 2021 May;22(5):58-68. doi: 10.1002/acm2.13246. Epub 2021 May 4.
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.
虚拟解剖体模提供了精确的体素映射，可用于基于真实变形矢量场（DVFs）的解剖结构变化。当测试可变形配准（DIR）算法时，Dice 相似系数（DSC）和平均一致性距离（MDA）是评估几何轮廓一致性的标准指标。使用 HN 虚拟患者体模数据集进行了 kVCT-kVCT 自动传播轮廓验证研究，采用 Accuray DIR 算法。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，以研究模态间 kVCT-MVCT DIR 准确性。研究结果表明，Accuray DIR 算法平均可以在 TG-132 推荐的容限范围内（DSC 0.8-0.9，体素宽度内的 MDA）充分自动传播 HN 轮廓。然而，对剂量学计划至关重要的轮廓应始终进行视觉准确性验证。使用标准重建 MVCT 图像质量会导致与真实轮廓的一致性略有降低，但仍可接受。
