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A Feasibility Study on Using Neural Network for Dose Calculation in Radiation Treatment
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 Title & Authors
A Feasibility Study on Using Neural Network for Dose Calculation in Radiation Treatment
Lee, Sang Kyung; Kim, Yong Nam; Kim, Soo Kon;
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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.
Radiation therapy;Dose calculation;Neural network;
 Cited by
Independent dose verification system with Monte Carlo simulations using TOPAS for passive scattering proton therapy at the National Cancer Center in Korea, Physics in Medicine & Biology, 2017, 62, 19, 7598  crossref(new windwow)
Demarco JJ, Chetty IJ, Solberg TD. A Monte Carlo tutorial and the application for radiotherapy treatment planning. Med Dosim. 2002;27(1):43-50. crossref(new window)

Zhao Y, Mcakenzle M, Kirkby C, Fallone BG. Monte Carlo calculation of helical tomotherapy dose delivery. Med Phys. 2008;35(8):3491-3500. crossref(new window)

Wu X, Zhu Y. A neural network regression model for relative dose computation. Phys Med Biol. 2000;45:913-922. crossref(new window)

Blake SW. Artificial neural network modeling of megavoltage photon dose distributions. Phys Med Biol.2004;49:2515-2526. crossref(new window)

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. crossref(new window)

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. crossref(new window)

Moller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 1993;6:525-533. crossref(new window)

Hagan MT, Menhaj M. Training feedforward networks with the Marquardt Algorithms, IEEE T Neural Networks. 1994;5(6):989-993. crossref(new window)