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\begin{document}

\title{Gridification of a Radiotherapy Dose Computation Application with the XtremWeb-CH Environment}

\author{Nabil Abdennhader\inst{1} \and Raphaël Couturier\inst{1} \and David \and
  Julien  Henriet\inst{2} \and  Laiymani\inst{1}  \and Sébastien  Miquée\inst{1}
  \and Marc Sauget\inst{2}}

\institute{Laboratoire d'Informatique de l'universit\'{e}
  de Franche-Comt\'{e} \\
  IUT Belfort-Montbéliard, Rue Engel Gros, 90016 Belfort - France \\
\email{raphael.couturier, david.laiymani, sebastien.miquee@univ-fcomte.fr}
\and
 FEMTO-ST, ENISYS/IRMA, F-25210 Montb\'{e}liard , FRANCE\\
}
%\email{\texttt{[laiymani]@lifc.univ-fcomte.fr}}}


\maketitle

\begin{abstract} 
  
\end{abstract}

%-------------INTRODUCTION--------------------
\section{Introduction}

The use of distributed architectures for solving large scientific problems seems
to  become  mandatory  in  a  lot  of  cases. For  example,  in  the  domain  of
radiotherapy dose computation the problem  is crucial. The main goal of external
beam  radiotherapy is  the treatment  of  tumours while  minimizing exposure  to
healthy tissue. Dosimetric  planning has to be carried out  in order to optimize
the dose distribution within the patient is necessary. Thus, for determining the
most accurate dose distribution during  treatment planning, a compromise must be
found between  the precision and  the speed of calculation.  Current techniques,
using   analytic   methods,  models   and   databases,   are   rapid  but   lack
precision. Enhanced precision can be  achieved by using calculation codes based,
for example, on Monte Carlo methods. In [] the authors proposed a novel approach
based on the use of neural  networks. The approach is based on the collaboration
of  computation  codes  and   multi-layer  neural  networks  used  as  universal
approximators. It provides a fast  and accurate evaluation of radiation doses in
any given environment for given  irradiation parameters. As the learning step is
often very time consumming, in \cite{bcvsv08:ip} the authors proposed a parallel
algorithm  that enable  to decompose  the learning  domain into  subdomains. The
decomposition has  the advantage to  significantly reduce the complexity  of the
target functions to approximate.

Now,  as  there  exist  several classes  of  distributed/parallel  architectures
(supercomputers,  clusters, global  computing...)  we have  to  choose the  best
suited  one  for  the  parallel  Neurad application.  The  Global  or  Volunteer
computing model seems  to be an interesting approach.  Here, the computing power
is obtained  by agregating unused  (or volunteer) public resources  connected to
the Internet.  For our  case, we  can imagine for  example, that  a part  of the
architecture  will  be  composed of  some  of  the  different computers  of  the
hospital. This approach present the advantage  to be clearly cheaper than a more
dedicated approach like the use of supercomputer or clusters.

The aim of this  paper is to propose and evaluate a  gridification of the Neurad
application (more precisely, of the most time consuming part, the learning step)
using  a  Global computing  approach.  For this,  we  focus  on the  XtremWeb-CH
environnement []. We choose this  environnent because it tackles the centralized
aspect of other global computing environments such as XTremWeb [] or Seti []. It
tends  to  a  peer-to-peer  approach  by distributing  some  components  of  the
architecture.  For  instance,  the  computing  nodes  are  allowed  to  directly
communicate.   Experimentations  were  conducted  on  a  real  Global  Computing
testbed. The results are very  encouraging. They exhibit an interesting speed-up
and show that the overhead induced by the use of XTremWeb-CH is very acceptable.

The  paper  is  organized  as  follows.  In section  2  we  present  the  Neurad
application  and particularly  it most  time  consuming part  i.e. the  learning
step.  Section 3 details  the XtremWeb-CH  environnement while  in section  4 we
expose  the gridification of  the Neurad  application. Experimental  results are
presented in section  5 and we end  in section 6 by some  concluding remarks and
perspectives.

\section{The Neurad application}

\begin{figure}[http]
  \centering
  \includegraphics[width=0.7\columnwidth]{figures/neurad.pdf}
  \caption{The Neurad projects}
  \label{f_neurad}
\end{figure}

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 algorithm with a HPU topology to do the training of our neural network.

The  Figure~\ref{f_neurad} presents  the {\it{Neurad}}  scheme. Three  parts are
clearly independant : the initial  data production, the learning process and the
dose deposit  evaluation.  The first step,  the data production,  is outside the
{\it{Neurad}}  project.  They  are  many  solutions to  obtains  data about  the
radiotherapy treatments like the measure  or the simulation.  The only essential
criterion is  that the result  must be obtain  in a homogeneous  environment. We
have chosen  to use  only a Monte  Carlo simulation  because this tools  are the
references in the radiotherapy domains. The advantages to use data obtain with a
Monte Carlo  simulator are the  following : accuracy, profusing,  quantify error
and regularity  of measure point.  But,  they are too disagreement  and the most
important  is  the  statistical  noise  forcing  a  data  post  treatment.   The
Figure~\ref{f_tray} present the general behavior of a dose deposit in water.


\begin{figure}[http]
  \centering
  \includegraphics[width=0.7\columnwidth]{figures/testC.pdf}
  \caption{Dose deposit by a photon beam  of 24 mm of width in water (Normalized value). }
  \label{f_tray}
\end{figure}

The secondary stage of the {\it{Neurad}}  project is about the learning step and
it is the most time consuming step. This step is off-line but is it important to
reduce the time used for the  learning process to keep a workable tools. Indeed,
if the learning time is too important (for the moment, this time could reach one
week for a  limited works domain), the  use of this process could  be be limited
only at a major modification of  the use context.  However, it is interesting to
do  an update to  the learning  process when  the bound  of the  learning domain
evolves (evolution in material used for  the prosthesis or evolution on the beam
(size, shape  or energy)). The learning time  is linked with the  volume of data
who could  be very important  in real medical  context.  We have work  to reduce
this  learning time  with a  parallel  method of  the learning  process using  a
partitioning method of  the global dataset. The goal of this  method is to train
many neural networks  on sub-domain of the global  dataset.  After this training,
the use  of this neural  networks together allows  to obtain a response  for the
global domain of study.


\begin{figure}[h]
  \centering
  \includegraphics[width=0.5\columnwidth]{figures/overlap.pdf}
  \caption{Overlapping for a sub-network  in a two-dimensional domain with ratio
    $\alpha$.}
  \label{fig:overlap}
\end{figure}


However, performing the learnings on sub-domains constituting a partition of the
initial domain is  not satisfying according to the quality  of the results. This
comes from the fact that the accuracy of the approximation performed by a neural
network is not  constant over the learned domain.  Thus, it  is necessary to use
an  overlapping  of the  sub-domains.   The  overall  principle is  depicted  in
Figure~\ref{fig:overlap}.   In this  way, each  sub-network has  an exploitation
domain  smaller than its  training domain  and the  differences observed  at the
borders  are  no  longer  relevant.   Nonetheless,  in  order  to  preserve  the
performances of  the parallel  algorithm, it is  important to carefully  set the
overlapping  ratio $\alpha$.   It must  be large  enough to  avoid  the border's
errors, and as small as possible to limit the size increase of the data subsets.





\section{The XtremWeb-CH environment}
\section{The XtremWeb-CH environment (\textit{XWCH})}
\input{xwch.tex}
\section{Experimental results}
\section{Conclusion and future works}



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