-The \emph{Neurad}~\cite{Neurad} project presented in this paper takes
-place in a multi-disciplinary project, involving medical physicists
-and computer scientists whose goal is to enhance the treatment
-planning of cancerous tumors by external radiotherapy. In our previous
-works~\cite{RADIO09,ICANN10,NIMB2008}, we have proposed an original
-approach to solve scientific problems whose accurate modeling and/or
-analytical description are difficult. That method is based on the
-collaboration of computational codes and neural networks used as
-universal interpolator. Thanks to that method, the \emph{Neurad}
-software provides a fast and accurate evaluation of radiation doses in
-any given environment (possibly inhomogeneous) for given irradiation
-parameters. We have shown in a previous work (\cite{AES2009}) the
-interest to use a distributed algorithm for the neural network
-learning. We use a classical RPROP (DEFINITION)algorithm with a HPU
-topology to do the training of our neural network.
-
-Figure~\ref{f_neurad} presents the {\it{Neurad}} scheme. Three parts
-are clearly independent: the initial data production, the learning
-process and the dose deposit evaluation. The first step, the data
-production, is outside of the {\it{Neurad}} project. They are many
-solutions to obtain data about the radiotherapy treatments like the
-measure or the simulation. The only essential criterion is that the
-result must be obtained in an homogeneous environment.
+The \emph{Neurad}~\cite{Neurad} project presented in this paper takes place in a
+multi-disciplinary project, involving medical physicists and computer scientists
+whose goal is to enhance the treatment planning of cancerous tumors by external
+radiotherapy. In our previous works~\cite{RADIO09,ICANN10,NIMB2008}, we have
+proposed an original approach to solve scientific problems whose accurate
+modeling and/or analytical description are difficult. That method is based on
+the collaboration of computational codes and neural networks used as universal
+interpolator. Thanks to that method, the \emph{Neurad} software provides a fast
+and accurate evaluation of radiation doses in any given environment (possibly
+inhomogeneous) for given irradiation parameters. We have shown in a previous
+work (\cite{AES2009}) the interest to use a distributed algorithm for the neural
+network learning. We use a classical RPROP~\footnote{Resilient backpropagation}
+algorithm with a HPU~\footnote{High order processing units} topology to do the
+training of our neural network.
+
+Figure~\ref{f_neurad} presents the {\it{Neurad}} scheme. Three parts are clearly
+independent: the initial data production, the learning process and the dose
+deposit evaluation. The first step, the data production, is outside of the
+{\it{Neurad}} project. They are many solutions to obtain data about the
+radiotherapy treatments like the measure or the simulation. The only essential
+criterion is that the result must be obtained in an homogeneous environment.