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放射学中的信息学:为临床研究和临床试验中的功能成像数据分析开发研究型 PACS。

Informatics in radiology: development of a research PACS for analysis of functional imaging data in clinical research and clinical trials.

机构信息

Cancer Research UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, 15 Cotswold Rd, Sutton SM2 5NG, England.

出版信息

Radiographics. 2012 Nov-Dec;32(7):2135-50. doi: 10.1148/rg.327115138. Epub 2012 Aug 28.

Abstract

Picture archiving and communication systems (PACS) provide limited flexibility for the development of novel research methods. By contrast, the research model of data access is more flexible but has vulnerabilities in numerous areas. No single monolithic application can fulfill the diverse and rapidly changing needs of the clinical imaging research community. Instead, the focus should be on the interoperability of preexisting systems. To a large extent, this can be achieved by means of a unified interface for storing and retrieving data. The concept of a research PACS combines the advantages of the clinical and research models of data access while eliminating the disadvantages. A research PACS streamlines the data management process. Instead of a single software program, it consists of a confederation of independent applications brought together by the ability to store and retrieve data in a common database. A prototype research PACS has been developed that is based on the Extensible Neuroimaging Archive Toolkit (XNAT) in association with two new in-house tools: a data selection tool and a data archiving tool. By taking as an example the comparison of regions of interest in multifunctional liver data, it was demonstrated that this framework allows a number of in-house and open-source applications originally designed to work on a stand-alone basis to be integrated into a unified workflow, with minimal redevelopment effort.

摘要

影像归档和通信系统 (PACS) 在开发新的研究方法方面提供的灵活性有限。相比之下,数据访问的研究模型更灵活,但在许多领域都存在漏洞。没有单一的整体应用程序可以满足临床成像研究界多样化且快速变化的需求。相反,重点应该放在现有系统的互操作性上。在很大程度上,这可以通过存储和检索数据的统一接口来实现。研究型 PACS 将临床和数据访问研究模型的优势结合在一起,同时消除了劣势。研究型 PACS 简化了数据管理流程。它不是一个单独的软件程序,而是由一组独立的应用程序组成,这些应用程序通过在公共数据库中存储和检索数据的能力结合在一起。已经开发了一个研究型 PACS 原型,它基于可扩展神经影像学归档工具包 (XNAT),并结合了两个新的内部工具:数据选择工具和数据归档工具。通过以多功能肝脏数据的感兴趣区域比较为例,证明了该框架允许一些最初设计用于独立工作的内部和开源应用程序集成到一个统一的工作流程中,只需进行最小的重新开发工作。

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