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使用低分辨率探测器的多通道混合网络进行错误检测,用于患者特定的质量保证

Bing Yan^1,2, Jun Shi³, Xudong Xue⁴

¹School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei, China.

Journal of applied clinical medical physics

|March 15, 2024

概括

一个新的混合多通道网络准确地检测了强度调节辐射疗法 (IMRT) 质量保证 (QA) 中的多叶聚合器 (MLC) 位置错误. 这种方法通过使用低分辨率探测器的剂量差异和马图来提高IMRT QA的安全性和可靠性.

科学领域:

医学物理医学物理
辐射瘤学辐射瘤学
医疗保健中的机器学习

背景情况:

患者特异性质量保证 (QA) 对强度调节辐射疗法 (IMRT) 至关重要.
检测MLC的位置错误对于IMRT的安全性和有效性至关重要.
低分辨率检测器经常用于质量保证,需要强大的错误检测方法.

研究的目的:

开发一种混合多通道网络,用于检测MLC的位置错误.
使用低分辨率探测器生成的剂量差异 (DD) 图和马图.
为了验证该方法在IMRT的患者特定QA中.

主要方法:

在68个IMRT计划中模拟了四种类型的MLC位置错误 (转移,打开,关闭,随机).
使用治疗计划系统 (TPS) 计算预测剂量,并使用PTW seven29幻影获得测量剂量分布.
生成DD和马地图,以创建3222个样本的数据集,用于网络培训和评估.
将混合多通道网络与ResNet-18和Swin-Transformer等基线方法进行比较.

主要成果:

混合多通道网络实现了卓越的性能,平均精度高 (0.87),准确度高 (0.89),灵敏度高 (0.85),特异性高 (0.97),F1得分高 (0.85).
该网络显示,随机错误 (0.964) 和无错误类别 (0.946) 的AUC值较高.
在无错误类别的精度上显著超过基线方法,尽管整体精度的边际差异很小.

结论:

拟议的多通道混合网络使用低分辨率探测器准确识别MLC错误.
这种方法为改善IMRTQA质量和安全提供了有效和可靠的解决方案.

关键词:

深度学习是一种深度学习.错误检测检测错误检测错误检测错误检测错误检测错误检测错误检测错误检测强度调节的辐射疗法.多通道混合网络多通道混合网络.患者特定的QA.

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