{Reference Type}: Journal Article
{Title}: Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.
{Author}: Samadi Miandoab P;Saramad S;Setayeshi S;
{Journal}: J Appl Clin Med Phys
{Volume}: 24
{Issue}: 3
{Year}: Mar 2023
{Factor}: 2.243
{DOI}: 10.1002/acm2.13854
{Abstract}: <B>BACKGROUND: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.