Department of Medical Physics, University Hospital "Maggiore della Carità", Novara, Italy.
Medical Physics Department, Veneto Institute of Oncology IOV IRCCS, Padua, Italy.
Med Phys. 2018 Feb;45(2):748-757. doi: 10.1002/mp.12737. Epub 2018 Jan 9.
To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms.
Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets. Head-and-neck (HN), thorax, and pelvis sites were included. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Statistical inference on validation results was carried out in order to identify the prognostic factors of DIR performances.
DVF intensity, anatomic site and participating center were significant prognostic factors of DIR performances. Sub-voxel accuracy was obtained in the HN by all algorithms. Large errors, with MDC ranging up to 6 mm, were observed in low-contrast regions that underwent significant deformation, such as in the pelvis, or large DVF with strong contrast, such as the clinical tumor volume (CTV) in the lung. Under these conditions, the hybrid DIR algorithms performed significantly better than the free-form intensity based algorithms and resulted robust against intercenter variability.
The performances of the systems proved to be site specific, depending on the DVF type and the platforms and the procedures used at the various centers. The pelvis was the most challenging site for most of the algorithms, which failed to achieve sub-voxel accuracy. Improved reproducibility was observed among the centers using the same hybrid registration algorithm.
研究使用多种商业平台通过变形图像配准（DIR）传播感兴趣区域（ROI）的各种算法的性能。
13 家机构参与了这项研究，涉及 6 个商业平台：RayStation（瑞典斯德哥尔摩的 RaySearch Laboratories）、MIM（美国克利夫兰）、VelocityAI 和 Smart Adapt（美国帕洛阿尔托的 Varian Medical Systems）、Mirada XD（英国牛津的 Mirada Medical Ltd）和 ABAS（瑞典斯德哥尔摩的 Elekta AB）。通过将两个特定的变形向量场（DVF）应用于真实患者数据集，使用 OncoImsys 公司的 ImSimQA 软件包（英国什鲁斯伯里）生成合成图像，对 DIR 算法进行了测试。纳入了头颈部（HN）、胸部和骨盆部位。使用 Dice 相似系数（DSC）和平均符合距离（MDC）指标，通过比较每个中心的 DIR 映射 ROI 与参考 ROI，评估算法的准确性。为了识别 DIR 性能的预后因素，对验证结果进行了统计推断。
DVF 强度、解剖部位和参与中心是 DIR 性能的重要预后因素。所有算法均在 HN 中实现了亚像素精度。在经历了明显变形的低对比度区域（如骨盆）或对比度大、变形大的区域（如肺中的临床肿瘤体积（CTV）），会观察到高达 6mm 的大误差。在这些条件下，混合 DIR 算法的性能明显优于基于自由形态强度的算法，并且对中心间变异性具有很强的稳健性。
系统性能取决于 DVF 类型以及各个中心使用的平台和程序，因此具有特定于站点的特征。对于大多数算法而言，骨盆是最具挑战性的部位，无法实现亚像素精度。使用相同的混合注册算法的中心之间观察到了更高的可重复性。
