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Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, Mario Cannataro (Ed.) (Ed.) (2009) 363-391
Grid Analysis of Radiological Data
Cecile Germain-Renaud1, 2, Vincent BRETON3, Patrick Clarysse4, Bertrand Delhay4, Yann Gaudeau5, Tristan Glatard4, Emmanuel Jeannot6, Yannick Legre3, Johan Montagnat7, Jean-Marie Moureaux5, Angel Osorio8, Xavier Pennec9, Joel Schaerer4, Romain Texier7

Grid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services.
1 :  LRI - Laboratoire de Recherche en Informatique
2 :  INRIA Saclay - Ile de France - TAO
3 :  LPC - Laboratoire de Physique Corpusculaire [Clermont-Ferrand]
4 :  CREATIS - Centre de recherche en applications et traitement de l'image pour la santé
5 :  CRAN - Centre de Recherche en Automatique de Nancy
7 :  Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe MODALIS
8 :  LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [Orsay]
9 :  INRIA Sophia Antipolis - ASCLEPIOS
Equipe PCSV
Informatique/Calcul parallèle, distribué et partagé
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