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放射学中的信息学:DICOM 标准的信息模型。

Informatics in radiology: an information model of the DICOM standard.

机构信息

Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, Wis 53226, USA.

出版信息

Radiographics. 2011 Jan-Feb;31(1):295-304. doi: 10.1148/rg.311105085. Epub 2010 Oct 27.

DOI:10.1148/rg.311105085
PMID:20980665
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3399709/
Abstract

The Digital Imaging and Communications in Medicine (DICOM) Standard is a key foundational technology for radiology. However, its complexity creates challenges for information system developers because the current DICOM specification requires human interpretation and is subject to nonstandard implementation. To address this problem, a formally sound and computationally accessible information model of the DICOM Standard was created. The DICOM Standard was modeled as an ontology, a machine-accessible and human-interpretable representation that may be viewed and manipulated by information-modeling tools. The DICOM Ontology includes a real-world model and a DICOM entity model. The real-world model describes patients, studies, images, and other features of medical imaging. The DICOM entity model describes connections between real-world entities and the classes that model the corresponding DICOM information entities. The DICOM Ontology was created to support the Cancer Biomedical Informatics Grid (caBIG) initiative, and it may be extended to encompass the entire DICOM Standard and serve as a foundation of medical imaging systems for research and patient care.

摘要

医学数字成像和通信(DICOM)标准是放射学的一项关键基础技术。然而,其复杂性给信息系统开发者带来了挑战,因为当前的 DICOM 规范需要人工解释,并且存在非标准的实现。为了解决这个问题,创建了一个形式正确且计算上可访问的 DICOM 标准信息模型。DICOM 标准被建模为本体,这是一种机器可访问且人类可解释的表示形式,可以通过信息建模工具进行查看和操作。DICOM 本体包括现实世界模型和 DICOM 实体模型。现实世界模型描述了患者、研究、图像和其他医学成像特征。DICOM 实体模型描述了现实世界实体与建模相应 DICOM 信息实体的类之间的连接。创建 DICOM 本体是为了支持癌症生物医学信息网格(caBIG)计划,它可以扩展以包含整个 DICOM 标准,并作为研究和患者护理的医学成像系统的基础。

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本文引用的文献

1
Cancer informatics vision: caBIG.癌症信息学愿景:癌症生物信息学网格(caBIG)
Cancer Inform. 2007 Feb 6;2:22-4.
2
Scaling the walls of discovery: using semantic metadata for integrative problem solving.攀登发现之墙:利用语义元数据进行综合问题解决。
Brief Bioinform. 2009 Mar;10(2):164-76. doi: 10.1093/bib/bbp007.
3
Biomedical ontologies in action: role in knowledge management, data integration and decision support.生物医学本体的应用:在知识管理、数据集成和决策支持中的作用。
Yearb Med Inform. 2008:67-79.
4
GraphWeb: mining heterogeneous biological networks for gene modules with functional significance.GraphWeb:挖掘具有功能意义的基因模块的异质生物网络
Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W452-9. doi: 10.1093/nar/gkn230. Epub 2008 May 6.
5
Biomedical ontologies: a functional perspective.生物医学本体:功能视角
Brief Bioinform. 2008 Jan;9(1):75-90. doi: 10.1093/bib/bbm059. Epub 2007 Dec 12.
6
The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration.OBO铸造厂:本体的协同进化以支持生物医学数据整合。
Nat Biotechnol. 2007 Nov;25(11):1251-5. doi: 10.1038/nbt1346.
7
The Cancer Biomedical Informatics Grid (caBIG): infrastructure and applications for a worldwide research community.癌症生物医学信息学网格(caBIG):面向全球研究团体的基础设施与应用
Stud Health Technol Inform. 2007;129(Pt 1):330-4.
8
Creating and curating a terminology for radiology: ontology modeling and analysis.创建和管理放射学术语:本体建模与分析。
J Digit Imaging. 2008 Dec;21(4):355-62. doi: 10.1007/s10278-007-9073-0. Epub 2007 Sep 15.
9
Open source software projects of the caBIG In Vivo Imaging Workspace Software special interest group.caBIG体内成像工作区软件特别兴趣小组的开源软件项目。
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