Abstract：
BACKGROUND: In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural networks to propose an accurate and reliable prediction model.
METHODS: Seven deep predictor models are trained and tested with 800 breathing signals. In this regard, a nonsequential-correlated hyperparameter optimization algorithm is developed to find the best configuration of parameters for all models. The root mean square error (RMSE), mean absolute error, normalized RMSE, and statistical F-test are also used to evaluate network performance.
RESULTS: Overall, tuning the hyperparameters results in a 25%-30% improvement for all models compared to previous studies. The comparison between all models also shows that the gated recurrent unit (GRU) with RMSE = 0.108 ± 0.068 mm predicts respiratory signals with higher accuracy and better performance.
CONCLUSIONS: Overall, tuning the hyperparameters in the GRU model demonstrates a better result than the hybrid predictor model used in the CyberKnife VSI system to compensate for the 115 ms system latency. Additionally, it is demonstrated that the tuned parameters have a significant impact on the prediction accuracy of each model.
METHODS: Seven deep predictor models are trained and tested with 800 breathing signals. In this regard, a nonsequential-correlated hyperparameter optimization algorithm is developed to find the best configuration of parameters for all models. The root mean square error (RMSE), mean absolute error, normalized RMSE, and statistical F-test are also used to evaluate network performance.
RESULTS: Overall, tuning the hyperparameters results in a 25%-30% improvement for all models compared to previous studies. The comparison between all models also shows that the gated recurrent unit (GRU) with RMSE = 0.108 ± 0.068 mm predicts respiratory signals with higher accuracy and better performance.
CONCLUSIONS: Overall, tuning the hyperparameters in the GRU model demonstrates a better result than the hybrid predictor model used in the CyberKnife VSI system to compensate for the 115 ms system latency. Additionally, it is demonstrated that the tuned parameters have a significant impact on the prediction accuracy of each model.
摘要：
背景：在外束放射治疗中，需要一个预测模型来补偿影响辐射剂量输送准确性的时间系统延迟。本研究集中于对七个深度人工神经网络的全面比较，以提出准确可靠的预测模型。
方法：用800个呼吸信号对7个深度预测模型进行了训练和测试。在这方面,开发了一种非序列相关的超参数优化算法，以找到所有模型的最佳参数配置。均方根误差(RMSE)，平均绝对误差，归一化RMSE，统计F检验也用于评估网络性能。
结果：总体而言，与以前的研究相比，调整超参数可以使所有模型提高25%-30%。所有模型之间的比较还表明，RMSE=0.108±0.068mm的门控递归单位（GRU）以更高的精度和更好的性能预测呼吸信号。
结论：总体而言，调整GRU模型中的超参数显示出比CyberKnifeVSI系统中使用的混合预测模型更好的结果，以补偿115毫秒的系统延迟。此外,结果表明，调整后的参数对每个模型的预测精度都有显著影响。
方法：用800个呼吸信号对7个深度预测模型进行了训练和测试。在这方面,开发了一种非序列相关的超参数优化算法，以找到所有模型的最佳参数配置。均方根误差(RMSE)，平均绝对误差，归一化RMSE，统计F检验也用于评估网络性能。
结果：总体而言，与以前的研究相比，调整超参数可以使所有模型提高25%-30%。所有模型之间的比较还表明，RMSE=0.108±0.068mm的门控递归单位（GRU）以更高的精度和更好的性能预测呼吸信号。
结论：总体而言，调整GRU模型中的超参数显示出比CyberKnifeVSI系统中使用的混合预测模型更好的结果，以补偿115毫秒的系统延迟。此外,结果表明，调整后的参数对每个模型的预测精度都有显著影响。
