The compilation and analysis of radiological images poses numerous challenges for researchers. The sheer volume of data as well as the computational needs of algorithms capable of operating on images are extensive. Additionally, the assembly of these images alone is difficult, as these exams may differ widely in terms of clinical context, structured annotation available for model training, modality, and patient identifiers. In this paper, we describe our experiences and challenges in establishing a trusted collection of radiology images linked to the United States Department of Veterans Affairs (VA) electronic health record database. We also discuss implications in making this repository research-ready for medical investigators. Key insights include uncovering the specific procedures required for transferring images from a clinical to a research-ready environment, as well as roadblocks and bottlenecks in this process that may hinder future efforts at automation.

翻译：放射学影像的整理与分析为研究者带来了诸多挑战。影像数据体量庞大，且能处理此类图像的算法对计算资源需求极高。此外，仅完成影像的收集本身就困难重重，因这些检查在临床背景、可供模型训练的结构化标注、成像模态及患者标识符等方面可能存在巨大差异。本文阐述了我们在建立与美国退伍军人事务部（VA）电子健康记录数据库相关联的可信放射学影像集合过程中的经验与挑战。我们还探讨了如何使该影像库达到供医学研究者使用的研究就绪状态。关键见解包括：揭示将影像从临床环境迁移至研究就绪环境所需的具体流程，以及在此过程中可能阻碍未来自动化工作的障碍与瓶颈。