Utilizing data grid architecture for the backup and recovery of clinical image data

Abstract 

Grid Computing represents the latest and most exciting technology to evolve from the familiar realm of parallel, peer-to-peer and client-server models. However, there has been limited investigation into the impact of this emerging technology in medical imaging and informatics. In particular, PACS technology, an established clinical image repository system, while having matured significantly during the past ten years, still remains weak in the area of clinical image data backup. Current solutions are expensive or time consuming and the technology is far from foolproof. Many large-scale PACS archive systems still encounter downtime for hours or days, which has the critical effect of crippling daily clinical operations. In this paper, a review of current backup solutions will be presented along with a brief introduction to grid technology. Finally, research and development utilizing the grid architecture for the recovery of clinical image data, in particular, PACS image data, will be presented.

The focus of this paper is centered on applying a grid computing architecture to a DICOM environment since DICOM has become the standard for clinical image data and PACS utilizes this standard. A federation of PACS can be created allowing a failed PACS archive to recover its image data from others in the federation in a seamless fashion. The design reflects the five-layer architecture of grid computing: Fabric, Resource, Connectivity, Collective, and Application Layers. The testbed Data Grid is composed of one research laboratory and two clinical sites. The Globus 3.0 Toolkit (Co-developed by the Argonne National Laboratory and Information Sciences Institute, USC) for developing the core and user level middleware is utilized to achieve grid connectivity.

The successful implementation and evaluation of utilizing data grid architecture for clinical PACS data backup and recovery will provide an understanding of the methodology for using Data Grid in clinical image data backup for PACS, as well as establishment of benchmarks for performance from future grid technology improvements. In addition, the testbed can serve as a road map for expanded research into large enterprise and federation level data grids to guarantee CA (Continuous Availability, 99.999% up time) in a variety of medical data archiving, retrieval, and distribution scenarios.

Keywords: PACS, Grid computing, Fault tolerance, Disaster recovery