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Abstract：
OBJECTIVE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process.
METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light-radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6-month period.
RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field-matched junction and ball-bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%.
CONCLUSIONS: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.
METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light-radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6-month period.
RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field-matched junction and ball-bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%.
CONCLUSIONS: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.
摘要：
目的：放射治疗中的线性加速器质量保证（QA）是确保辐射输送机器性能特性的耗时但基本的部分。这项工作的目标是在肿瘤学信息系统（OIS）中开发自动化和标准化的QA计划生成和分析系统，以简化QA流程。
方法：自动化QA流程包括两个软件组件：AutoQABuilder，每月,季度,治疗计划系统（TPS）和AutoQA分析中的各种周期性线性加速器QA计划，以分析在电子门户成像设备（EPID）上收集的图像，从而可以快速分析所获取的QA图像。为了验证自动QA分析的结果，结果与下颌交界处质量保证评估的现行标准进行比较，光辐射巧合，栅栏,和体积调节电弧治疗（VMAT）QA计划跨越三个直线和6个月的时间。
结果：AutoQABuilder应用程序已在临床上使用了322次，用于创建QA患者，构建幻像，并跨多个机构部署常见的定期QA测试，线性加速器,和物理学家。将AutoQA分析结果与我们当前的机构QA标准进行比较，场匹配接头和滚珠轴承位置检测中强度值比率的平均差为0.012±0.053（P=0.159），为0.011±0.224mm（P=0.355），分别。对VMATQA计划的分析得出的最大百分比差异为0.3%。
结论：使用多个API对质量保证计划进行自动化创建和分析，对直线加速器质量保证效率和标准化具有直接的益处。QA计划的创建无需通过API协助遵循繁琐的程序即可完成,并且分析可以在临床OIS内以自动化方式进行。
方法：自动化QA流程包括两个软件组件：AutoQABuilder，每月,季度,治疗计划系统（TPS）和AutoQA分析中的各种周期性线性加速器QA计划，以分析在电子门户成像设备（EPID）上收集的图像，从而可以快速分析所获取的QA图像。为了验证自动QA分析的结果，结果与下颌交界处质量保证评估的现行标准进行比较，光辐射巧合，栅栏,和体积调节电弧治疗（VMAT）QA计划跨越三个直线和6个月的时间。
结果：AutoQABuilder应用程序已在临床上使用了322次，用于创建QA患者，构建幻像，并跨多个机构部署常见的定期QA测试，线性加速器,和物理学家。将AutoQA分析结果与我们当前的机构QA标准进行比较，场匹配接头和滚珠轴承位置检测中强度值比率的平均差为0.012±0.053（P=0.159），为0.011±0.224mm（P=0.355），分别。对VMATQA计划的分析得出的最大百分比差异为0.3%。
结论：使用多个API对质量保证计划进行自动化创建和分析，对直线加速器质量保证效率和标准化具有直接的益处。QA计划的创建无需通过API协助遵循繁琐的程序即可完成,并且分析可以在临床OIS内以自动化方式进行。
