University College London Hospitals NHS Foundation Trust, 250 Euston Road, London, United Kingdom. St. Bartholomew's Hospital, West Smithfield, London, United Kingdom. Author to whom any correspondence should be addressed.
Phys Med Biol. 2018 Aug 1;63(15):155014. doi: 10.1088/1361-6560/aad1bb.
Changes in lung architecture during a course of radiotherapy can alter the planned dose distribution to the extent that it becomes clinically unacceptable. This study aims to validate a quantitative method of determining whether a replan is required during the course of conformal radiotherapy. The proposed method uses deformable image registration (DIR) to flexibly map planning CT (pCT) data to the anatomy of online CBCT images. The resulting deformed CT (dCT) images are used as a basis for assessing the effect of anatomical change on dose distributions. The study used retrospective data from a sample of seven replanned lung patients. The settings of an in-house, open-source DIR algorithm were first optimised for CT-to-CBCT registrations of the anatomy of the thorax. Using these optimised parameters, each patient's pCT was deformed to the CBCT acquired immediately before the replan. Registration accuracy was rigorously validated both geometrically and dosimetrically to confirm that the dCTs could reliably be used to inform replan decisions. A retrospective evaluation of the changes in dose delivered over time was then carried out for a single patient to demonstrate the clinical application of the proposed method. The geometric analysis showed good agreement between deformed structures and those same structures manually outlined on the CBCT images. Results were consistently better than those achieved with rigid-only registration. In the dosimetric analysis, dose distributions derived from the dCTs were found to match closely to the 'gold standard' replan CT (rCT) distributions across dose volume histogram and absolute dose difference measures. The retrospective analysis of serial CBCTs of a single patient produced reliable quantitative assessment of the dose delivery. Had the proposed method been available at the time of treatment, it would have enabled a more objective replan decision. DIR is a valuable clinical tool for dose recalculation in adaptive radiotherapy protocols for lung cancer patients.
在放射治疗过程中，肺结构的变化可能会改变计划的剂量分布，使其在临床上无法接受。本研究旨在验证一种定量方法，以确定在适形放疗过程中是否需要重新计划。该方法使用可变形图像配准（DIR）灵活地将计划 CT（pCT）数据映射到在线 CBCT 图像的解剖结构上。生成的变形 CT（dCT）图像可用于评估解剖变化对剂量分布的影响。该研究使用了来自 7 例重新计划的肺癌患者的回顾性数据。首先，针对胸部解剖结构的 CT 到 CBCT 配准，对内部的开源 DIR 算法的设置进行了优化。使用这些优化的参数，将每个患者的 pCT 变形为在重新计划前采集的 CBCT。对配准精度进行了严格的几何和剂量学验证，以确认 dCT 可以可靠地用于指导重新计划决策。然后，对一名患者的时间推移的剂量分布进行了回顾性评估，以展示所提出方法的临床应用。几何分析表明，变形结构与在 CBCT 图像上手动勾画的结构具有很好的一致性。结果始终优于仅使用刚性配准的结果。在剂量学分析中，发现从 dCT 中得出的剂量分布与“黄金标准”重新计划 CT（rCT）分布在剂量体积直方图和绝对剂量差异度量上非常匹配。对单个患者的一系列 CBCT 的回顾性分析产生了对剂量输送的可靠定量评估。如果在治疗时就可以使用所提出的方法，那么它将能够做出更客观的重新计划决策。DIR 是用于肺癌患者自适应放疗方案中剂量重算的有价值的临床工具。
