目的:本研究旨在开发一种混合多通道网络,以使用剂量差(DD)图和从低分辨率探测器生成的伽马图检测多叶准直器(MLC)位置误差在患者特定的质量保证(QA)中进行调强放射治疗(IMRT)。
方法:本研究共包括68个计划,358束IMRT。修改了原始IMRT计划中所有控制点的MLC叶片位置,以模拟四种类型的误差:移位误差,打开错误,关闭错误,和随机错误。将这些修改后的计划导入治疗计划系统(TPS)以计算预测剂量,而PTWsever29体模用于获得测量的剂量分布。根据测量和预测的剂量,DD地图和伽马地图,有错误和没有错误,产生了,产生具有3222个样本的数据集。使用各种指标评估了网络的性能,包括准确性,灵敏度,特异性,精度,F1分数,ROC曲线,和归一化混淆矩阵。此外,其他基线方法,如单通道混合网络,ResNet-18和双变压器,也作为比较进行了评估。
结果:实验结果表明,多通道混合网络优于其他方法,表现出更高的平均精度,准确度,灵敏度,特异性,和F1分数,值分别为0.87、0.89、0.85、0.97和0.85。多通道混合网络还在随机误差(0.964)和无误差(0.946)类别中实现了更高的AUC值。尽管多通道混合网络的平均精度仅略高于ResNet-18和SwinTransformer,在无错误类别的精度方面,它明显优于它们。
结论:所提出的多通道混合网络在使用低分辨率检测器识别MLC错误方面表现出很高的准确性。该方法为提高IMRTQA的质量和安全性提供了有效可靠的解决方案。
OBJECTIVE: This study aimed to develop a hybrid multi-channel network to detect multileaf collimator (MLC) positional errors using dose difference (DD) maps and gamma maps generated from low-resolution detectors in patient-specific quality assurance (QA) for Intensity Modulated Radiation Therapy (IMRT).
METHODS: A total of 68 plans with 358 beams of IMRT were included in this study. The MLC leaf positions of all control points in the original IMRT plans were modified to simulate four types of errors: shift error, opening error, closing error, and random error. These modified plans were imported into the treatment planning system (TPS) to calculate the predicted dose, while the PTW seven29 phantom was utilized to obtain the measured dose distributions. Based on the measured and predicted dose, DD maps and gamma maps, both with and without errors, were generated, resulting in a dataset with 3222 samples. The network\'s performance was evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1-score, ROC curves, and normalized confusion matrix. Besides, other baseline methods, such as single-channel hybrid network, ResNet-18, and Swin-Transformer, were also evaluated as a comparison.
RESULTS: The experimental results showed that the multi-channel hybrid network outperformed other methods, demonstrating higher average precision, accuracy, sensitivity, specificity, and F1-scores, with values of 0.87, 0.89, 0.85, 0.97, and 0.85, respectively. The multi-channel hybrid network also achieved higher AUC values in the random errors (0.964) and the error-free (0.946) categories. Although the average accuracy of the multi-channel hybrid network was only marginally better than that of ResNet-18 and Swin Transformer, it significantly outperformed them regarding precision in the error-free category.
CONCLUSIONS: The proposed multi-channel hybrid network exhibits a high level of accuracy in identifying MLC errors using low-resolution detectors. The method offers an effective and reliable solution for promoting quality and safety of IMRT QA.