Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network | |
Yu SM(郁树梅)1; Wang, Jiateng1; Liu JG(刘金国)2; Sun RC(孙荣川)1; Kuang, Shaolong1; Sun LN(孙立宁)1 | |
刊名 | IEEE Access |
2020 | |
卷号 | 8页码:49424-49435 |
关键词 | Radiosurgery respiratory motion predicting Bi-GRU LSTM |
ISSN号 | 2169-3536 |
产权排序 | 2 |
英文摘要 | In chest and abdomen robotic radiosurgery, due to the motion delay of the robotic manipulator, the tumor position tracking process has a period of delay. This delay ultimately affects the accuracy of radiosurgery treatment. To address the influence of the delay in robotic radiosurgery, a Long-and-Short-Term Memory (LSTM) network as a deep Recurrent Neural Network (RNN) has been applied in a prediction network model for respiratory motion tracking in recent years. However, patients' respiratory state may change in the process of treatment, which may influence the accuracy of prediction. Therefore, it is necessary to update the prediction network through additional data, such as the actual position of the tumor obtained by X-ray imaging. However, the LSTM network has a long update time, and it may not be able to complete the prediction model update in a cycle of X-ray acquisition. To solve this problem, a fast prediction model based on Bidirectional Gated Recurrent Unit (Bi-GRU), is proposed in this paper. This method can reduce the average updating time of the network model by 30%. |
资助项目 | National Natural Science Foundation of China[61773273] |
WOS关键词 | BREATH-HOLD TECHNIQUE ; NEURAL-NETWORK ; MODEL |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
WOS记录号 | WOS:000524733100007 |
资助机构 | National Natural Science Foundation of China through the project |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/26646] |
专题 | 沈阳自动化研究所_空间自动化技术研究室 |
通讯作者 | Sun RC(孙荣川) |
作者单位 | 1.School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China 2.Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China |
推荐引用方式 GB/T 7714 | Yu SM,Wang, Jiateng,Liu JG,et al. Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network[J]. IEEE Access,2020,8:49424-49435. |
APA | Yu SM,Wang, Jiateng,Liu JG,Sun RC,Kuang, Shaolong,&Sun LN.(2020).Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network.IEEE Access,8,49424-49435. |
MLA | Yu SM,et al."Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network".IEEE Access 8(2020):49424-49435. |
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