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首页> 外文期刊>Computerized Medical Imaging and Graphics: The Official Jounal of the Computerized Medical Imaging Society >Utilizing data grid architecture for the backup and recovery of clinical image data.
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Utilizing data grid architecture for the backup and recovery of clinical image data.

机译:利用数据网格体系结构来备份和恢复临床图像数据。

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摘要

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.
机译:网格计算代表了从并行,对等和客户端-服务器模型的熟悉领域发展而来的最新,最令人兴奋的技术。但是,对这项新兴技术在医学成像和信息学中的影响的研究很少。尤其是,PACS技术,一种已建立的临床图像存储系统,尽管在过去十年中已显着成熟,但在临床图像数据备份方面仍然很薄弱。当前的解决方案昂贵或耗时,并且该技术远非万无一失。许多大型PACS存档系统仍然会遇到数小时或数天的停机时间,这严重影响了日常临床运营。在本文中,将对当前的备份解决方案进行回顾,并简要介绍网格技术。最后,将介绍利用网格体系结构恢​​复临床图像数据(特别是PACS图像数据)的研究和开发。本文的重点是将网格计算体系结构应用于DICOM环境,因为DICOM已成为临床图像数据的标准,而PACS则利用此标准。可以创建PACS联合,允许失败的PACS存档以无缝方式从联合中的其他人恢复其图像数据。该设计反映了网格计算的五层体系结构:结构,资源,连接性,集合层和应用程序层。测试床数据网格由一个研究实验室和两个临床站点组成。 Globus 3.0工具包(由USC的Argonne国家实验室和信息科学研究所共同开发)用于开发核心和用户级中间件,用于实现网格连接。利用数据网格体系结构进行临床PACS数据备份和恢复的成功实施和评估,将有助于理解在PACS的临床图像数据备份中使用数据网格的方法,并为未来网格技术的改进建立性能基准。此外,测试床可以作为路线图,用于将研究扩展到大型企业和联邦级别的数据网格,以确保在各种医疗数据归档,检索和分发场景中的CA(连续可用性,正常运行时间为99.999%)。

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