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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
Department空间自动化技术研究室
Source PublicationIEEE Access
ISSN2169-3536
2020
Volume8Pages:49424-49435
Indexed BySCI ; EI
EI Accession number20201308360435
WOS IDWOS:000524733100007
Contribution Rank2
Funding OrganizationNational Natural Science Foundation of China through the project
KeywordRadiosurgery respiratory motion predicting Bi-GRU LSTM
Abstract

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%.

Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordBREATH-HOLD TECHNIQUE ; NEURAL-NETWORK ; MODEL
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational Natural Science Foundation of China[61773273]
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26646
Collection空间自动化技术研究室
Corresponding AuthorSun RC(孙荣川)
Affiliation1.School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China
2.Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China
Recommended Citation
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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