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Investigation of error detection capabilities of phantom EPID and MLC log file based IMRT QA methods

机译:基于IMRT QA方法的幻影EPID和MLC日志文件的错误检测功能的研究

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摘要

A patient specific quality assurance (QA) should detect errors that originate anywhere in the treatment planning process. However, the increasing complexity of treatment plans has increased the need for improvements in the accuracy of the patient specific pretreatment verification process. This has led to the utilization of higher resolution QA methods such as the electronic portal imaging device (EPID) as well as MLC log files and it is important to know the types of errors that can be detected with these methods. In this study, we will compare the ability of three QA methods (Delta4®, MU‐EPID, Dynalog QA) to detect specific errors. Multileaf collimator (MLC) errors, gantry angle, and dose errors were introduced into five volumetric modulated arc therapy (VMAT) plans for a total of 30 plans containing errors. The original plans (without errors) were measured five times with each method to set a threshold for detectability using two standard deviations from the mean and receiver operating characteristic (ROC) derived limits. Gamma passing percentages as well as percentage error of planning target volume (PTV) were used for passing determination. When applying the standard 95% pass rate at 3%/3 mm gamma analysis errors were detected at a rate of 47, 70, and 27% for the Delta4, MU‐EPID and Dynalog QA respectively. When using thresholds set at 2 standard deviations from our base line measurements errors were detected at a rate of 60, 30, and 47% for the Delta4, MU‐ style="fixed-case">EPID and Dynalog style="fixed-case">QA respectively. When using style="fixed-case">ROC derived thresholds errors were detected at a rate of 60, 27, and 47% for the Delta4, style="fixed-case">MU‐ style="fixed-case">EPID and Dynalog style="fixed-case">QA respectively. When using dose to the style="fixed-case">PTV and the Dynalog method 11 of the 15 small style="fixed-case">MLC errors were detected while none were caught using gamma analysis. A combination of the style="fixed-case">EPID and Dynalog style="fixed-case">QA methods (scaling Dynalog doses using style="fixed-case">EPID images) matches the detection capabilities of the Delta4 by adding additional comparison metrics. These additional metrics are vital in relating the style="fixed-case">QA measurement to the dose received by the patient which is ultimately what is being confirmed.
机译:特定于患者的质量保证(QA)应该检测出在治疗计划过程中任何地方产生的错误。但是,治疗计划的复杂性不断增加,因此需要提高针对患者的特定治疗前验证过程的准确性。这导致使用更高分辨率的QA方法,例如电子门户成像设备(EPID)以及MLC日志文件,因此了解使用这些方法可以检测到的错误的类型非常重要。在这项研究中,我们将比较三种QA方法(Delta 4 ®,MU-EPID,Dynalog QA)检测特定错误的能力。将多叶准直仪(MLC)误差,机架角度和剂量误差引入了五个容积调制弧光治疗(VMAT)计划中,总共有30个计划包含误差。每种方法对原始计划(无误差)进行了五次测量,以使用与均值和接收器工作特性(ROC)得出的限值相差两个标准偏差来设置可检测性的阈值。伽玛通过率百分比以及计划目标体积(PTV)的百分比误差用于通过判定。当以3%/ 3 mm的伽玛标准通过率95%时,对于Delta 4 ,MU-EPID和Dynalog QA,分析误差分别为47%,70%和27%。当使用阈值设置为与基线测量值有2个标准偏差时,对于Delta 4 ,MU- style =“ fixed-case”,检出错误的比率为60%,30%和47% > EPID 和Dynalog style =“ fixed-case”>质量检查。当使用 style =“ fixed-case”> ROC 导出的阈值时,Delta 4 , style =“ fixed-case“> MU - style =” fixed-case“> EPID 和Dynalog style =” fixed-case“> QA 。在对 style =“ fixed-case”> PTV 使用剂量和Dynalog方法11时,检测到15个小的 style =“ fixed-case”> MLC 错误,而没有发现使用伽玛分析捕获。 style =“ fixed-case”> EPID 和Dynalog style =“ fixed-case”> QA 方法的组合(使用 style =“ fixed-case “> EPID 图片)通过添加其他比较指标来匹配Delta 4 的检测功能。这些额外的指标对于将 style =“ fixed-case”> QA 测量值与患者所接受的剂量相关联至关重要,而这最终是可以确定的。

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