R Texier | PubMed Health

PubMed2005

Grid-enabling medical image analysis.

Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. 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. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.

Computer Communication Networks↗

PubMed2005

Interactive volume reconstruction and measurement on the Grid.

OBJECTIVES: To prove the advantages of integrating grid computing within medical image analysis software, and to discuss the technological, sociological and health care-related issues. METHODS: Presentation of an instant volume reconstruction and measurement tool (PTM3D) used in clinical practice, including percutaneous nephrolithotomy examples; description of a parallel implementation of volume reconstruction, evaluation of this implementation on lung and body reconstruction, presentation of the technical limitations for clinical use and description and discussion of a prototype grid implementation. RESULTS: Volume reconstruction can broaden its medical scope and use by accessing high-performance computing systems; interactive exploration of medical images can co-exist with the usual batch workload of grid systems; the EGEE grid middleware offers some of the required core services; a fully adequate computing environment needs further evolution to integrate realtime constraints. CONCLUSIONS: Clinical experiments of a grid-enabled PTM3D become possible. Widespread adoption of grid technology in the medical images analysis field will benefit from this "early user" project. Convergences appear between two broadly different fields, high energy physics and medical image, towards the need of a smooth integration of the new resources offered by grid systems into the everyday tools of their respective end-users. It can be expected that the convergence will mature towards truly interactive grids, able to serve the needs of the medical community.

Algorithms↗