Binny Diana, Aland Trent, Archibald-Heeren Ben R, Trapp Jamie V, Kairn Tanya, Crowe Scott B
Icon Cancer Centres, Northlakes, QLD, Australia.
Queensland University of Technology, Brisbane, QLD, Australia.
J Appl Clin Med Phys. 2019 Mar;20(3):71-80. doi: 10.1002/acm2.12547. Epub 2019 Feb 20.
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).
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.
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.
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.
针对独立探测器对自动化集成机器性能检查(MPC)工具进行了验证,以评估其射束均匀性和输出检测能力,从而确定其是否适用于日常质量保证(QA)。
使用临床可用的光子和电子能量,在六台直线加速器(每台位于六个独立地点)上进行测量,为期长达12个月(n = 350)。将每日对射束对称性和输出的稳定性检查结果与独立设备进行比较,如SNC每日QA 3、PTW Farmer电离室和SNC射野尺寸QA模体。还评估了MPC对射束对称性调整的均匀性检测。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的公差和基线重置,以考虑输出读数的漂移。I控制图用于评估系统和非系统趋势,以基于使用来自平均数据集的标准偏差计算出的上控制限和下控制限(UCL/LCL)来提高错误检测能力。
本研究调查了供应商的均匀性检测方法。计算得出的平均均匀性变化在Daily QA 3垂直对称性测量值的±0.5%以内。MPC输出的平均变化在Daily QA 3的±1.5%以内,在Farmer电离室检测到的变化的±0.5%以内。SPC计算的UCL值是MPC和Daily QA 3检测到的输出中观察到的变化量度。
机器性能检查被验证为一种日常质量保证工具,可用于检查机器输出和对称性,同时每周与独立探测器进行比对评估。通过基于SPC的定期趋势分析可以改进MPC输出检测能力,以测量固有设备中的漂移并控制系统和随机变化,从而增强对其作为QA设备能力的信心。基于SPC能力和可接受性计算,建议每三个月进行一次MPC校准评估。