Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network. (3rd August 2017)

• Record Type: Journal Article
• Authors: Sun, W Z; Jiang, M Y; Ren, L; Dang, J; You, T; Yin, F-F
• Is Part Of: Physics in medicine & biology. Volume 62:Number 17(2017:Sep.)
• Page Start: 6822
• Page End: 6835
• Publication Date: 2017-08-03
• Subjects: radiation therapy -- dynamic tracking -- artificial neural network -- respiratory motion prediction -- multi-layer perceptron neural network; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa7cd4
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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 500 ms 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 500 ms 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.