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BioMed Research International
Volume 2015 (2015), Article ID 489679, 13 pages
Research Article

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

1Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, 16607 Prague, Czech Republic
2Department of Radiological Imaging and Informatics, Graduate School of Medicine, Tohoku University, Sendai 980-8575, Japan
3Division on Advanced Information Technology, Yoshizawa Laboratory, Tohoku University, Sendai 980-8578, Japan

Received 3 June 2014; Accepted 1 September 2014

Academic Editor: Tsair-Fwu Lee

Copyright © 2015 Ivo Bukovsky et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.