In patient-specific quality assurance (QA) for static beam intensity-modulated radiation therapy (IMRT), machine-learning-based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two-dimensional detector, they have not been extended to the analysis of volumetric-modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient-specific VMAT QA.

中文翻译：

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使用多任务卷积神经网络开发的错误检测模型用于体积调制弧光疗法的患者特定质量保证

在针对静态光束强度调制放射治疗 (IMRT) 的患者特定质量保证 (QA) 中，已经开发了基于机器学习的剂量分析方法来识别错误原因，作为伽马分析的替代方法。尽管这些新方法表明可以通过分析从二维探测器获得的剂量分布来确定误差的原因，但它们还没有扩展到体积调制弧光疗法 (VMAT) QA 的分析。在这项研究中，我们提出了一种深度学习方法来检测特定于患者的 VMAT QA 中的各种类型的错误。