Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study. Issue 3 (1st December 2022) - Samadi Miandoab, Payam; Saramad, Shahyar; Setayeshi, Saeed
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. Conclusion: 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.