Predicting respiratory motion using an Informer deep learning network
10.3760/cma.j.cn112271-20221120-00451
- VernacularTitle:利用Informer深度学习网络预测呼吸运动
- Author:
Guodong JIN
1
;
Yuxiang LIU
;
Bining YANG
;
Ran WEI
;
Xinyuan CHEN
;
Xiaokun LIANG
;
Hong QUAN
;
Kuo MEN
;
Jianrong DAI
Author Information
1. 武汉大学物理科学与技术学院，武汉 430072
- Keywords:
Respiratory motion;
Deep learning;
Time series forecasting
- From:
Chinese Journal of Radiological Medicine and Protection
2023;43(7):513-517
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate a time series deep learning model for respiratory motion prediction.Methods:Eighty pieces of respiratory motion data from lung cancer patients were used in this study. They were divided into a training set and a test set at a ratio of 8∶2. The Informer deep learning network was employed to predict the respiratory motions with a latency of about 600 ms. The model performance was evaluated based on normalized root mean square errors (nRMSEs) and relative root mean square errors (rRMSEs).Results:The Informer model outperformed the conventional multilayer perceptron (MLP) and long short-term memory (LSTM) models. The Informer model yielded an average nRMSE and rRMSE of 0.270 and 0.365, respectively, at a prediction time of 423 ms, and 0.380 and 0.379, respectively, at a prediction time of 615 ms.Conclusions:The Informer model performs well in the case of a longer prediction time and has potential application value for improving the effects of the real-time tracking technology.
