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Grid Analysis of Radiological Data

Grid Analysis of Radiological Data

Cecile Germain-Renaud, Vincent Breton, Patrick Clarysse, Bertrand Delhay, Yann Gaudeau, Tristan Glatard, Emmanuel Jeannot, Yannick Legre
Copyright: © 2009 |Pages: 29
ISBN13: 9781605663746|ISBN10: 1605663743|EISBN13: 9781605663753
DOI: 10.4018/978-1-60566-374-6.ch019
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MLA

Germain-Renaud, Cecile, et al. "Grid Analysis of Radiological Data." Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare, edited by Mario Cannataro, IGI Global, 2009, pp. 363-391. https://doi.org/10.4018/978-1-60566-374-6.ch019

APA

Germain-Renaud, C., Breton, V., Clarysse, P., Delhay, B., Gaudeau, Y., Glatard, T., Jeannot, E., & Legre, Y. (2009). Grid Analysis of Radiological Data. In M. Cannataro (Ed.), Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare (pp. 363-391). IGI Global. https://doi.org/10.4018/978-1-60566-374-6.ch019

Chicago

Germain-Renaud, Cecile, et al. "Grid Analysis of Radiological Data." In Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare, edited by Mario Cannataro, 363-391. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-374-6.ch019

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Abstract

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.

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