Passarge Michelle, Fix Michael K, Manser Peter, Stampanoni Marco F M, Siebers Jeffrey V
Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital Bern University Hospital and University of Bern, Berne, 3010, Switzerland.
Department of Radiation Oncology, University of Virginia Health System, Charlottesville, 22908, Virginia, USA.
Med Phys. 2017 Apr;44(4):1212-1223. doi: 10.1002/mp.12142. Epub 2017 Mar 17.
To develop a robust and efficient process that detects relevant dose errors (dose errors of ≥5%) in external beam radiation therapy and directly indicates the origin of the error. The process is illustrated in the context of electronic portal imaging device (EPID)-based angle-resolved volumetric-modulated arc therapy (VMAT) quality assurance (QA), particularly as would be implemented in a real-time monitoring program.
A Swiss cheese error detection (SCED) method was created as a paradigm for a cine EPID-based during-treatment QA. For VMAT, the method compares a treatment plan-based reference set of EPID images with images acquired over each 2° gantry angle interval. The process utilizes a sequence of independent consecutively executed error detection tests: an aperture check that verifies in-field radiation delivery and ensures no out-of-field radiation; output normalization checks at two different stages; global image alignment check to examine if rotation, scaling, and translation are within tolerances; pixel intensity check containing the standard gamma evaluation (3%, 3 mm) and pixel intensity deviation checks including and excluding high dose gradient regions. Tolerances for each check were determined. To test the SCED method, 12 different types of errors were selected to modify the original plan. A series of angle-resolved predicted EPID images were artificially generated for each test case, resulting in a sequence of precalculated frames for each modified treatment plan. The SCED method was applied multiple times for each test case to assess the ability to detect introduced plan variations. To compare the performance of the SCED process with that of a standard gamma analysis, both error detection methods were applied to the generated test cases with realistic noise variations.
Averaged over ten test runs, 95.1% of all plan variations that resulted in relevant patient dose errors were detected within 2° and 100% within 14° (<4% of patient dose delivery). Including cases that led to slightly modified but clinically equivalent plans, 89.1% were detected by the SCED method within 2°. Based on the type of check that detected the error, determination of error sources was achieved. With noise ranging from no random noise to four times the established noise value, the averaged relevant dose error detection rate of the SCED method was between 94.0% and 95.8% and that of gamma between 82.8% and 89.8%.
An EPID-frame-based error detection process for VMAT deliveries was successfully designed and tested via simulations. The SCED method was inspected for robustness with realistic noise variations, demonstrating that it has the potential to detect a large majority of relevant dose errors. Compared to a typical (3%, 3 mm) gamma analysis, the SCED method produced a higher detection rate for all introduced dose errors, identified errors in an earlier stage, displayed a higher robustness to noise variations, and indicated the error source.
开发一种强大且高效的流程，用于检测外照射放疗中的相关剂量误差（≥5%的剂量误差）并直接指出误差来源。该流程在基于电子射野影像装置（EPID）的角度分辨容积调强弧形放疗（VMAT）质量保证（QA）的背景下进行说明，特别是在实时监测程序中实施的情况。
创建了一种瑞士奶酪误差检测（SCED）方法，作为基于电影EPID的治疗期间QA的范例。对于VMAT，该方法将基于治疗计划的EPID图像参考集与在每个2°机架角度间隔采集的图像进行比较。该流程利用一系列独立连续执行的误差检测测试：孔径检查，用于验证野内辐射传输并确保无野外辐射；在两个不同阶段的输出归一化检查；全局图像配准检查，以检查旋转、缩放和平移是否在公差范围内；像素强度检查，包括标准伽马评估（3%，3毫米）以及包含和排除高剂量梯度区域的像素强度偏差检查。确定了每个检查的公差。为了测试SCED方法，选择了12种不同类型的误差来修改原始计划。为每个测试案例人工生成一系列角度分辨的预测EPID图像，从而为每个修改后的治疗计划生成一系列预先计算的帧。对每个测试案例多次应用SCED方法，以评估检测引入的计划变化的能力。为了将SCED流程的性能与标准伽马分析的性能进行比较，将两种误差检测方法应用于具有实际噪声变化的生成测试案例。
在十次测试运行中平均而言，所有导致相关患者剂量误差的计划变化中有95.1%在2°内被检测到，100%在14°内被检测到（<患者剂量输送的4%）。包括导致轻微修改但临床等效计划的案例，SCED方法在2°内检测到了89.1%。根据检测到误差的检查类型，实现了误差来源的确定。在噪声范围从无随机噪声到既定噪声值的四倍的情况下，SCED方法的平均相关剂量误差检测率在94.0%至95.8%之间，伽马分析的平均相关剂量误差检测率在82.8%至89.8%之间。
通过模拟成功设计并测试了一种基于EPID帧的VMAT输送误差检测流程。使用实际噪声变化对SCED方法的稳健性进行了检验，表明它有潜力检测到绝大多数相关剂量误差。与典型的（3%，3毫米）伽马分析相比，SCED方法对所有引入的剂量误差具有更高的检测率，能在更早阶段识别误差，对噪声变化具有更高的稳健性，并能指出误差来源。
