The Study on Predicting Respiratory Motion with Support Vector Regression

被引：0

作者：

Tong, Lei ^{[1
]}

Chen, Chaomin ^{[2
]}

Kang, Kailian ^{[2
]}

Xu, Zihai ^{[3
]}

机构：

[1] Guangdong Vocat Coll Mech & Elect Technol, Guangzhou 510515, Guangdong, Peoples R China

[2] Southern Med Univ, Inst Biomed Engn, Guangzhou 510515, Guangdong, Peoples R China

[3] PLA 303 Hosp, Radiotherapy Ctr, Nanning 530021, Peoples R China

来源：

PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON COMPUTER MODELING, SIMULATION AND ALGORITHM (CMSA 2018) | 2018年 / 151卷

关键词：

radiotherapy; support vector regression; respiratory motion prediction; kernel function;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Objective: The target is usually tracked in real time at thoracic and abdominal radiotherapy due to the effect of respiratory motion, the prediction is necessary to compensate the system latency. Method: This paper proposed a prediction method based on support vector regression, it dynamically updates the training set and achieves the accurate online support vector regression. Result: The experiment selected seven respiratory motion data, using online model trained and predicted. The mean absolute error was 0.30mm. Conclusion: The online accurate support vector regression described respiratory motion accurately, and the results with high precision can be satisfied in practical application.

引用

页码：204 / 207

页数：4

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