Secondary use of radiological imaging data: Vanderbilt’s ImageVU approach

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics Pub Date : 2025-09-10 DOI:10.1016/j.jbi.2025.104905

David S. Smith , Karthik Ramadass , Laura Jones , Jennifer Morse , Daniel Fabbri , Joseph R. Coco , Shunxing Bao , Melissa Basford , Peter J. Embi , Reed A. Omary , John C. Gore , Jill M. Pulley , Bennett A. Landman

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引用次数: 0

Abstract

Objective:

To develop ImageVU, a scalable research imaging infrastructure that integrates clinical imaging data with metadata-driven cohort discovery, enabling secure, efficient, and regulatory-compliant access to imaging for secondary and opportunistic research use. This manuscript presents a detailed description of ImageVU’s key components and lessons learned to assist other institutions in developing similar research imaging services and infrastructure.

Methods:

ImageVU was designed to support the secondary use of radiological imaging data through a dedicated research imaging store. The system comprises four interconnected components: a Research PACS, an Ad Hoc Backfill Host, Cloud Storage System, and a De-Identification System. Imaging metadata are extracted and stored in the Research Derivative (RD), an identified clinical data repository, and the Synthetic Derivative (SD), a de-identified research data repository, with access facilitated through the RD Discover web portal. Researchers interact with the system via structured metadata queries and multiple data delivery options, including web-based viewing, bulk downloads, and dataset preparation for high-performance computing environments.

Results:

The integration of metadata-driven search capabilities has streamlined cohort discovery and improved imaging data accessibility. As of December 2024, ImageVU has processed 12.9 million MRI and CT series from 1.36 million studies across 453,403 patients. The system has supported 75 project requests, delivering over 50 TB of imaging data to 55 investigators, leading to 66 published research papers.

Conclusion:

ImageVU demonstrates a scalable and efficient approach for integrating clinical imaging into research workflows. By combining institutional data infrastructure with cloud-based storage and metadata-driven cohort identification, the platform enables secure and compliant access to imaging for translational research.

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放射成像数据的二次使用：Vanderbilt的ImageVU方法

目的：开发ImageVU，一个可扩展的研究成像基础设施，将临床成像数据与元数据驱动的队列发现集成在一起，为二次和机会性研究提供安全、高效、符合法规的成像访问。本文详细介绍了ImageVU的关键组件和经验教训，以帮助其他机构开发类似的研究成像服务和基础设施。方法：ImageVU旨在通过专门的研究成像存储来支持放射成像数据的二次使用。该系统由四个相互连接的组件组成：研究PACS， Ad Hoc回填主机，云存储系统和去识别系统。影像元数据被提取并存储在研究衍生品（RD）和合成衍生品（SD）中，RD衍生品是一个已识别的临床数据存储库，合成衍生品（SD）是一个去识别的研究数据存储库，可以通过RD发现门户网站方便地访问。研究人员通过结构化元数据查询和多种数据交付选项与系统交互，包括基于web的查看、批量下载和高性能计算环境的数据集准备。结果：元数据驱动搜索功能的集成简化了队列发现并改善了成像数据的可访问性。截至2024年12月，ImageVU已经处理了1290万份MRI和CT系列，来自136万份研究，涉及453,403名患者。该系统已经支持了75个项目请求，为55名研究人员提供了超过50tb的成像数据，并发表了66篇研究论文。结论：ImageVU展示了一种可扩展和有效的方法，将临床成像整合到研究工作流程中。通过将机构数据基础设施与基于云的存储和元数据驱动的队列识别相结合，该平台可以安全、合规地访问转化研究的成像。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Biomedical Informatics 医学-计算机：跨学科应用

CiteScore

8.90

自引率

6.70%

发文量

243

审稿时长

32 days

期刊介绍： The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.