Title
Customized prediction of respiratory motion with clustering from multiple patient interaction
Abstract
Information processing of radiotherapy systems has become an important research area for sophisticated radiation treatment methodology. Geometrically precise delivery of radiotherapy in the thorax and upper abdomen is compromised by respiratory motion during treatment. Accurate prediction of the respiratory motion would be beneficial for improving tumor targeting. However, a wide variety of breathing patterns can make it difficult to predict the breathing motion with explicit models. We proposed a respiratory motion predictor, that is, customized prediction with multiple patient interactions using neural network (CNN). For the preprocedure of prediction for individual patient, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the intraprocedure, the proposed CNN used neural networks (NN) for a part of the prediction and the extended Kalman filter (EKF) for a part of the correction. The prediction accuracy of the proposed method was investigated with a variety of prediction time horizons using normalized root mean squared error (NRMSE) values in comparison with the alternate recurrent neural network (RNN). We have also evaluated the prediction accuracy using the marginal value that can be used as the reference value to judge how many signals lie outside the confidence level. The experimental results showed that the proposed CNN can outperform RNN with respect to the prediction accuracy with an improvement of 50%.
Year
DOI
Venue
2013
10.1145/2508037.2508050
ACM TIST
Keywords
Field
DocType
customized prediction,neural network,proposed cnn,prediction accuracy,breathing motion,accurate prediction,prediction time horizon,respiratory motion predictor,multiple patient interaction,respiratory motion,recurrent neural networks,intelligent systems,multilayer perceptron
Extended Kalman filter,Intelligent decision support system,Feature selection,Computer science,Recurrent neural network,Multilayer perceptron,Artificial intelligence,Breathing,Artificial neural network,Cluster analysis,Machine learning
Journal
Volume
Issue
ISSN
4
4
2157-6904
Citations 
PageRank 
References 
0
0.34
17
Authors
4
Name
Order
Citations
PageRank
Suk-jin Lee1417.74
Yuichi Motai223024.68
Elisabeth Weiss300.68
Shumei S. Sun400.68