| Jin Guodong,Liu Yuxiang,Yang Bining,et al.Predicting respiratory motion using an Informer deep learning network[J].Chinese Journal of Radiological Medicine and Protection,2023,43(7):513-517 |
| Predicting respiratory motion using an Informer deep learning network |
| Received:November 20, 2022 |
| DOI：10.3760/cma.j.cn112271-20221120-00451 |
| KeyWords:Respiratory motion|Deep learning|Time series forecasting |
| FundProject:国家自然科学基金(12175312);北京市科技新星计划(Z201100006820058) |
| Author Name | Affiliation | E-mail | | Jin Guodong | School of Physics and Technology, Wuhan University, Wuhan 430072, China | | | Liu Yuxiang | School of Physics and Technology, Wuhan University, Wuhan 430072, China | | | Yang Bining | Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Cancer, National Cancer Center, Beijing 100021, China | | | Wei Ran | Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Cancer, National Cancer Center, Beijing 100021, China | | | Chen Xinyuan | Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Cancer, National Cancer Center, Beijing 100021, China | | | Liang Xiaokun | Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China | | | Quan Hong | School of Physics and Technology, Wuhan University, Wuhan 430072, China | | | Men Kuo | Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Cancer, National Cancer Center, Beijing 100021, China | menkuo@cicams.ac.cn | | Dai Jianrong | Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Cancer, National Cancer Center, Beijing 100021, China | |
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| 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. |
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