Liu Shi, Mazur Thomas R, Li Harold, Curcuru Austen, Green Olga L, Sun Baozhou, Mutic Sasa, Yang Deshan
Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
J Appl Clin Med Phys. 2017 Jan;18(1):128-138. doi: 10.1002/acm2.12017. Epub 2016 Dec 8.
In this study, a method is reported to perform IMRT and VMAT treatment delivery verification using 3D volumetric primary beam fluences reconstructed directly from planned beam parameters and treatment delivery records. The goals of this paper are to demonstrate that 1) 3D beam fluences can be reconstructed efficiently, 2) quality assurance (QA) based on the reconstructed 3D fluences is capable of detecting additional treatment delivery errors, particularly for VMAT plans, beyond those identifiable by other existing treatment delivery verification methods, and 3) QA results based on 3D fluence calculation (3DFC) are correlated with QA results based on physical phantom measurements and radiation dose recalculations.
Using beam parameters extracted from DICOM plan files and treatment delivery log files, 3D volumetric primary fluences are reconstructed by forward-projecting the beam apertures, defined by the MLC leaf positions and modulated by beam MU values, at all gantry angles using first-order ray tracing. Treatment delivery verifications are performed by comparing 3D fluences reconstructed using beam parameters in delivery log files against those reconstructed from treatment plans. Passing rates are then determined using both voxel intensity differences and a 3D gamma analysis. QA sensitivity to various sources of errors is defined as the observed differences in passing rates. Correlations between passing rates obtained from QA derived from both 3D fluence calculations and physical measurements are investigated prospectively using 20 clinical treatment plans with artificially introduced machine delivery errors.
Studies with artificially introduced errors show that common treatment delivery problems including gantry angle errors, MU errors, jaw position errors, collimator rotation errors, and MLC leaf position errors were detectable at less than normal machine tolerances. The reported 3DFC QA method has greater sensitivity than measurement-based QA methods. Statistical analysis-based Spearman's correlations shows that the 3DFC QA passing rates are significantly correlated with passing rates of physical phantom measurement-based QA methods.
Among measurement-less treatment delivery verification methods, the reported 3DFC method is less demanding than those based on full dose re-calculations, and more comprehensive than those that solely checks beam parameters in treatment log files. With QA passing rates correlating to measurement-based passing rates, the 3DFC QA results could be useful for complementing the physical phantom measurements, or verifying treatment deliveries when physical measurements are not available. For the past 4+ years, the reported method has been implemented at authors' institution 1) as a complementary metric to physical phantom measurements for pretreatment, patient-specific QA of IMRT and VMAT plans, and 2) as an important part of the log file-based automated verification of daily patient treatment deliveries. It has been demonstrated to be useful in catching both treatment plan data transfer errors and treatment delivery problems.
在本研究中，报告了一种使用直接从计划射束参数和治疗执行记录重建的三维体积原射线注量来执行调强放射治疗（IMRT）和容积旋转调强放疗（VMAT）治疗执行验证的方法。本文的目标是证明：1）三维射束注量能够高效重建；2）基于重建的三维注量的质量保证（QA）能够检测出其他现有治疗执行验证方法无法识别的额外治疗执行误差，特别是对于VMAT计划；3）基于三维注量计算（3DFC）的QA结果与基于物理模体测量和辐射剂量重新计算的QA结果相关。
利用从DICOM计划文件和治疗执行日志文件中提取的射束参数，通过使用一阶射线追踪在所有机架角度向前投影由多叶准直器（MLC）叶片位置定义并由射束监测单位（MU）值调制的射束孔径，重建三维体积原射线注量。通过比较使用治疗执行日志文件中的射束参数重建的三维注量与从治疗计划重建的三维注量来进行治疗执行验证。然后使用体素强度差异和三维伽马分析确定通过率。QA对各种误差源的敏感度定义为观察到的通过率差异。使用20个人为引入机器执行误差的临床治疗计划，前瞻性地研究从三维注量计算和物理测量得出的QA通过率之间的相关性。
人为引入误差的研究表明，常见的治疗执行问题，包括机架角度误差、MU误差、准直器位置误差、准直器旋转误差和MLC叶片位置误差，在低于正常机器公差的情况下即可检测到。所报告的3DFC QA方法比基于测量的QA方法具有更高的敏感度。基于统计分析的斯皮尔曼相关性表明，3DFC QA通过率与基于物理模体测量的QA方法的通过率显著相关。
在无测量的治疗执行验证方法中，所报告的3DFC方法比基于全剂量重新计算的方法要求更低，比仅检查治疗日志文件中的射束参数的方法更全面。由于QA通过率与基于测量的通过率相关，3DFC QA结果可用于补充物理模体测量，或在无法进行物理测量时验证治疗执行情况。在过去4年多的时间里，所报告的方法已在作者所在机构实施：1）作为IMRT和VMAT计划预处理、患者特异性QA的物理模体测量的补充指标；2）作为基于日志文件的每日患者治疗执行自动验证的重要组成部分。它已被证明在发现治疗计划数据传输误差和治疗执行问题方面很有用。
