School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei, China.
Department of Radiation Oncology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China.
J Appl Clin Med Phys. 2024 Jun;25(6):e14327. doi: 10.1002/acm2.14327. Epub 2024 Mar 15.
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).
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.
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.
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.
本研究旨在开发一种混合多通道网络，以使用低分辨率探测器在强度调制放射治疗（IMRT）的患者特定质量保证（QA）中生成的剂量差（DD）图和伽马图来检测多叶准直器（MLC）位置误差。
本研究共纳入 68 个计划，共 358 束 IMRT。将原始 IMRT 计划中所有控制点的 MLC 叶片位置修改，以模拟四种类型的误差：移位误差、开启误差、关闭误差和随机误差。将这些修改后的计划导入治疗计划系统（TPS）计算预测剂量，同时使用 PTW seven29 体模获取测量剂量分布。基于测量和预测剂量，生成有无误差的 DD 图和伽马图，共得到 3222 个样本的数据集。使用各种指标评估网络的性能，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，还评估了其他基线方法，如单通道混合网络、ResNet-18 和 Swin-Transformer。
实验结果表明，多通道混合网络的性能优于其他方法，平均精度、准确性、灵敏度、特异性和 F1 分数分别为 0.87、0.89、0.85、0.97 和 0.85。多通道混合网络在随机误差（0.964）和无误差（0.946）类别中的 AUC 值也更高。尽管多通道混合网络的平均准确性仅略高于 ResNet-18 和 Swin Transformer，但在无误差类别中，其精度明显优于它们。
所提出的多通道混合网络在使用低分辨率探测器识别 MLC 误差方面具有很高的准确性。该方法为促进 IMRT QA 的质量和安全性提供了一种有效且可靠的解决方案。
