Browse > Article
http://dx.doi.org/10.14407/jrp.2015.40.1.055

A Feasibility Study on Using Neural Network for Dose Calculation in Radiation Treatment  

Lee, Sang Kyung (Kangwon National University Hospital)
Kim, Yong Nam (Kangwon National University Hospital)
Kim, Soo Kon (Kangwon National University, School of Medicine)
Publication Information
Journal of Radiation Protection and Research / v.40, no.1, 2015 , pp. 55-64 More about this Journal
Abstract
Dose calculations which are a crucial requirement for radiotherapy treatment planning systems require accuracy and rapid calculations. The conventional radiotherapy treatment planning dose algorithms are rapid but lack precision. Monte Carlo methods are time consuming but the most accurate. The new combined system that Monte Carlo methods calculate part of interesting domain and the rest is calculated by neural can calculate the dose distribution rapidly and accurately. The preliminary study showed that neural networks can map functions which contain discontinuous points and inflection points which the dose distributions in inhomogeneous media also have. Performance results between scaled conjugated gradient algorithm and Levenberg-Marquardt algorithm which are used for training the neural network with a different number of neurons were compared. Finally, the dose distributions of homogeneous phantom calculated by a commercialized treatment planning system were used as training data of the neural network. In the case of homogeneous phantom;the mean squared error of percent depth dose was 0.00214. Further works are programmed to develop the neural network model for 3-dimensinal dose calculations in homogeneous phantoms and inhomogeneous phantoms.
Keywords
Radiation therapy; Dose calculation; Neural network;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Demarco JJ, Chetty IJ, Solberg TD. A Monte Carlo tutorial and the application for radiotherapy treatment planning. Med Dosim. 2002;27(1):43-50.   DOI   ScienceOn
2 Zhao Y, Mcakenzle M, Kirkby C, Fallone BG. Monte Carlo calculation of helical tomotherapy dose delivery. Med Phys. 2008;35(8):3491-3500.   DOI   ScienceOn
3 Wu X, Zhu Y. A neural network regression model for relative dose computation. Phys Med Biol. 2000;45:913-922.   DOI   ScienceOn
4 Blake SW. Artificial neural network modeling of megavoltage photon dose distributions. Phys Med Biol.2004;49:2515-2526.   DOI   ScienceOn
5 Mathieu R, Martin E, Gschwind R, Makovicka L, Contassot-Vivier S, Bahi J. Calculations of dose distributions using a neural network model. Phys Med Biol. 2005;50:1019-1028.   DOI   ScienceOn
6 Vasseur A, Makovicka L, Martin E, Sauget M, Contassot-Vivier S, Bahi J. Dose calculations using artificial neural networks: A feasibility study for photon beams. Nucl Instrum Meth B. 2008;266:1085-1093.   DOI   ScienceOn
7 Moller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 1993;6:525-533.   DOI   ScienceOn
8 Hagan MT, Menhaj M. Training feedforward networks with the Marquardt Algorithms, IEEE T Neural Networks. 1994;5(6):989-993.   DOI   ScienceOn