Department of Medical Physics, University Hospital "Maggiore della Carità," Novara, Italy.
Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy.
Pract Radiat Oncol. 2020 Mar-Apr;10(2):125-132. doi: 10.1016/j.prro.2019.11.011. Epub 2019 Nov 28.
To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography.
Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets. On-board images from 3 systems were used: megavoltage computed tomography from Tomotherapy and 2 kinds of CBCT from a clinical linear accelerator. Image quality of the system was evaluated. The algorithms' accuracy was assessed by comparing the DIR-mapped ROIs returned by each center with those of the reference, using the Dice similarity coefficient and mean distance to conformity metrics. Statistical inference on the validation results was carried out to identify the prognostic factors of DIR performance.
Analyzing 840 DIR-mapped ROIs returned by the centers, it was demonstrated that DVF intensity and image quality were significant prognostic factors of DIR performance. The accuracy of the propagated contours was generally high, and acceptable DIR performance can be obtained with lower-dose CBCT image protocols.
The performance of the systems proved to be image quality specific, depending on the DVF type and only partially on the platforms. All systems proved to be robust against image artifacts and noise, except the demon-based software.
研究使用多种商业平台（从计算机断层扫描到锥形束计算机断层扫描（CBCT）和兆伏计算机断层扫描）传播感兴趣区域（ROI）的各种变形图像配准（DIR）算法的性能。
14 个机构使用 5 个商业平台参与了这项研究：RayStation（瑞典斯德哥尔摩的 RaySearch Laboratories）、MIM（俄亥俄州克利夫兰）、VelocityAI 和 SmartAdapt（加利福尼亚州帕洛阿尔托的 Varian Medical Systems）以及 ABAS（瑞典斯德哥尔摩的 Elekta AB）。通过将 2 个特定的变形向量场（DVF）应用于真实的头颈部患者数据集，使用 ImSimQA（英国什鲁斯伯里的 Oncology Systems Limited）软件包生成合成图像来测试算法。使用 3 个系统的机载图像：来自 Tomotherapy 的兆伏计算机断层扫描和来自临床线性加速器的 2 种 CBCT。评估系统的图像质量。通过比较每个中心返回的 DIR 映射 ROI 与参考 ROI 的 Dice 相似系数和一致性平均距离度量，评估算法的准确性。对验证结果进行统计推断，以确定 DIR 性能的预后因素。
分析中心返回的 840 个 DIR 映射 ROI，结果表明 DVF 强度和图像质量是 DIR 性能的重要预后因素。传播轮廓的准确性通常较高，并且可以使用低剂量 CBCT 图像协议获得可接受的 DIR 性能。
系统的性能证明是特定于图像质量的，取决于 DVF 类型，而仅部分取决于平台。除了基于示踪剂的软件外，所有系统都被证明对图像伪影和噪声具有鲁棒性。
