A multi-institutional evaluation of machine performance check system on treatment beam output and symmetry using statistical process control
A multi-institutional evaluation of machine performance check system on treatment beam output and symmetry using statistical process control
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DOI：
10.1002/acm2.12547
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发表时间：
2019-03-01
影响因子：
2.1
通讯作者：
Crowe, Scott B.
中科院分区：
文献类型：
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作者：
Binny, Diana;Aland, Trent;Crowe, Scott B.
背景 自动化集成机器性能检查 (MPC) 工具通过独立检测器进行验证，以评估其光束均匀性和输出检测能力，以考虑其适合日常质量保证 (QA)。方法 使用临床可用的光子和电子能量在六个直线加速器（每个位于六个单独的地点）上进行长达 12 个月的测量（n = 350）。将光束对称性和输出的日常稳定性检查与独立设备（例如 SNC Daily QA 3、PTW Farmer 电离室和 SNC 场尺寸 QA 体模）进行比较。还评估了光束对称性调整的 MPC 均匀性检测。使用统计过程控制 (SPC) 方法评估对称性和输出测量的灵敏度，以得出日常机器 QA 的容差和基线重置，以解决输出读数的漂移。 I 图表用于评估系统和非系统趋势，以根据使用平均数据集的标准差计算出的上限和下限控制水平 (UCL/LCL) 来提高错误检测能力。结果本研究调查了供应商的均匀性检测方法。计算出的平均均匀度变化在 Daily QA 3 垂直对称测量值的 +/- 0.5% 范围内。平均 MPC 输出变化在每日 QA 3 的 +/- 1.5% 范围内以及 Farmer 电离室检测到的变化的 +/- 0.5% 范围内。 SPC 计算的 UCL 值是 MPC 和每日 QA 3 检测到的输出中观察到的变化的衡量标准。 结论 机器性能检查被验证为每日质量保证工具，用于检查机器输出和对称性，同时每周根据独立检测器进行评估。 MPC 输出检测可以通过基于 SPC 的常规趋势分析来改进，以测量固有设备中的漂移并控制系统和随机变化，从而提高对其作为 QA 设备的能力的信心。建议根据 SPC 能力和可接受性计算进行每 3 个月一次的 MPC 校准评估。
Background The automated and integrated machine performance check (MPC) tool was verified against independent detectors to evaluate its beam uniformity and output detection abilities to consider it suitable for daily quality assurance (QA). Methods Measurements were carried out on six linear accelerators (each located at six individual sites) using clinically available photon and electron energies for a period up to 12 months (n = 350). Daily constancy checks on beam symmetry and output were compared against independent devices such as the SNC Daily QA 3, PTW Farmer ionization chamber, and SNC field size QA phantom. MPC uniformity detection of beam symmetry adjustments was also assessed. Sensitivity of symmetry and output measurements were assessed using statistical process control (SPC) methods to derive tolerances for daily machine QA and baseline resets to account for drifts in output readings. I-charts were used to evaluate systematic and nonsystematic trends to improve error detection capabilities based on calculated upper and lower control levels (UCL/LCL) derived using standard deviations from the mean dataset. Results This study investigated the vendor's method of uniformity detection. Calculated mean uniformity variations were within +/- 0.5% of Daily QA 3 vertical symmetry measurements. Mean MPC output variations were within +/- 1.5% of Daily QA 3 and +/- 0.5% of Farmer ionization chamber detected variations. SPC calculated UCL values were a measure of change observed in the output detected for both MPC and Daily QA 3. Conclusions Machine performance check was verified as a daily quality assurance tool to check machine output and symmetry while assessing against an independent detector on a weekly basis. MPC output detection can be improved by regular SPC-based trend analysis to measure drifts in the inherent device and control systematic and random variations thereby increasing confidence in its capabilities as a QA device. A 3-monthly MPC calibration assessment was recommended based on SPC capability and acceptability calculations.