Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network

被引:31
|
作者
Sun, W. Z. [1 ,2 ]
Jiang, M. Y. [1 ]
Ren, L. [2 ]
Dang, J. [3 ]
You, T. [3 ]
Yin, F-F [2 ]
机构
[1] Shandong Univ, Inst Informat Sci & Engn, Jinan, Shandong, Peoples R China
[2] Duke Univ, Dept Radiat Oncol, Ctr Canc, Durham, NC 27708 USA
[3] Jiangsu Univ, Dept Radiat Oncol, Affiliated Hosp, Zhenjiang, Jiangsu, Peoples R China
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2017年 / 62卷 / 17期
基金
美国国家卫生研究院;
关键词
radiation therapy; dynamic tracking; artificial neural network; respiratory motion prediction; multi-layer perceptron neural network; BREATH-HOLD; TUMOR TRACKING; MOTION; MODEL; LUNG; AVERAGE; FILTER;
D O I
10.1088/1361-6560/aa7cd4
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.
引用
收藏
页码:6822 / 6835
页数:14
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