Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, California; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China.
Department of Radiation Oncology, University of Kentucky, Lexington, Kentucky.
Pract Radiat Oncol. 2021 Sep-Oct;11(5):404-414. doi: 10.1016/j.prro.2021.02.012. Epub 2021 Mar 17.
This study aimed to evaluate the accuracy of deformable image registration (DIR) algorithms using data sets with different levels of ground-truth deformation vector fields (DVFs) and to investigate the correlation between DVF errors and contour-based metrics.
Nine pairs of digital data sets were generated through contour-controlled deformations based on 3 anonymized patients' CTs (head and neck, thorax/abdomen, and pelvis) with low, medium, and high deformation intensity for each site using the ImSimQA software. Image pairs and their associated contours were imported to MIM-Maestro, Raystation, and Velocity systems, followed by DIR and contour propagation. The system-generated DVF and propagated contours were compared with the ground-truth data. The correlation between DVF errors and contour-based metrics was evaluated using the Pearson correlation coefficient (r), while their correlation with volumes were calculated using Spearman correlation coefficient (rho).
The DVF errors increased with increasing deformation intensity. All DIR algorithms performed well for esophagus, trachea, left femoral, right femoral, and urethral (mean and maximum DVF errors <2.50 mm and <4.27 mm, respectively; Dice similarity coefficient: 0.93-0.99). Brain, liver, left lung, and bladder showed large DVF errors for all 3 systems (d: 2.8-91.90 mm). The minimum and maximum DVF errors, conformity index, and Dice similarity coefficient were correlated with volumes (|rho|: 0.41-0.64), especially for very large or small structures (|rho|: 0.64-0.80). Only mean distance to agreement of Raystation and Velocity correlated with some indices of DVF errors (r: 0.70-0.78).
Most contour-based metrics had no correlation with DVF errors. For adaptive radiation therapy, well-performed contour propagation does not directly indicate accurate dose deformation and summation/accumulation within each contour (determined by DVF accuracy). Tolerance values for DVF errors should vary as the acceptable accuracy for overall adaptive radiation therapy depends on anatomic site, deformation intensity, organ size, and so forth. This study provides benchmark tables for evaluating DIR accuracy in various clinical scenarios.
本研究旨在评估不同真实变形向量场(DVF)水平数据集的变形图像配准(DIR)算法的准确性,并研究 DVF 误差与基于轮廓的指标之间的相关性。
使用 ImSimQA 软件,基于 3 名匿名患者的 CT(头颈部、胸部/腹部和骨盆),在每个部位生成 9 对具有低、中、高变形强度的数字数据集,通过轮廓控制变形生成。将图像对及其相关轮廓导入 MIM-Maestro、Raystation 和 Velocity 系统,然后进行 DIR 和轮廓传播。将系统生成的 DVF 和传播的轮廓与真实数据进行比较。使用 Pearson 相关系数(r)评估 DVF 误差与基于轮廓的指标之间的相关性,使用 Spearman 相关系数(rho)评估它们与体积的相关性。
DVF 误差随变形强度的增加而增加。所有 DIR 算法在食管、气管、左股骨、右股骨和尿道(平均和最大 DVF 误差<2.50mm 和<4.27mm,分别;Dice 相似系数:0.93-0.99)方面表现良好。对于所有 3 个系统,大脑、肝脏、左肺和膀胱的 DVF 误差都很大(d:2.8-91.90mm)。最小和最大的 DVF 误差、一致性指数和 Dice 相似系数与体积相关(|rho|:0.41-0.64),特别是对于非常大或非常小的结构(|rho|:0.64-0.80)。仅 Raystation 和 Velocity 的平均距离一致性与某些 DVF 误差指标相关(r:0.70-0.78)。
大多数基于轮廓的指标与 DVF 误差无关。对于自适应放射治疗,良好的轮廓传播并不直接表明在每个轮廓内的准确剂量变形和求和/累积(由 DVF 精度决定)。DVF 误差的容差值应根据解剖部位、变形强度、器官大小等因素而变化,因为接受的自适应放射治疗的整体准确性取决于这些因素。本研究为在各种临床情况下评估 DIR 准确性提供了基准表。