Rorden Christopher, Béranger Benoît, Cheng Hu, Clemence Matthew, Debacker Clément, Fernandez Brice, Halchenko Yaroslav O, Harms Michael P, Holla Bharath, Innis Isaiah, Kuijer Joost P A, Levitas Daniel, Litinas Krisanne, Luci Jeffrey, Newman-Norlund Roger, Peltier Scott, Rehwald Wolfgang, Reid Robert I, Rogers Baxter, Schwarz Christopher G, Shin Jaemin, Ganesan Venkatasubramanian, Ganji Sandeep, Morgan Paul S
McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, 29208, USA.
CENIR, Paris Brain Institute - ICM, Hôpital Pitié-Salpêtrière de Sorbonne Université, Paris, France.
Sci Data. 2025 Jul 9;12(1):1168. doi: 10.1038/s41597-025-05503-w.
DICOM is an industry-standard for medical imaging data targeted at interoperability across systems. This enables transfer, storage and processing of imaging data regardless of the manufacturer. Pragmatically, manufacturers often store detailed acquisition parameters in private rather than public DICOM tags. In parallel, the DICOM standard itself has gradually evolved by introducing new public tags and properties to better capture emerging imaging technologies. Accurately extracting these details is essential for reproducible neuroimaging research. To address this need, we created a series of DICOM datasets illustrating how various manufacturers encode acquisition details that are critical for modern processing and analysis. These minimal test cases, covering CT and MR modalities, highlight manufacturer-specific conventions, including the use of public tags, private tags, and proprietary data structures. For each DICOM dataset, we provide corresponding NIfTI-formatted images with metadata JSON files following the BIDS standard, using consistent terminology to mitigate variations in how manufacturers encode acquisition details. Our repository provides validation datasets for any tool that is intended to extract acquisition details from medical imaging data.
DICOM是针对跨系统互操作性的医学成像数据行业标准。这使得成像数据能够在不考虑制造商的情况下进行传输、存储和处理。实际上,制造商通常将详细的采集参数存储在私有而非公共的DICOM标签中。与此同时,DICOM标准本身也通过引入新的公共标签和属性逐渐发展,以更好地涵盖新兴成像技术。准确提取这些细节对于可重复的神经成像研究至关重要。为满足这一需求,我们创建了一系列DICOM数据集,展示了不同制造商如何对现代处理和分析至关重要的采集细节进行编码。这些涵盖CT和MR模态的最小测试用例突出了特定于制造商的惯例,包括公共标签、私有标签和专有数据结构的使用。对于每个DICOM数据集,我们提供符合BIDS标准的带有元数据JSON文件的相应NIfTI格式图像,使用一致的术语来减轻制造商对采集细节进行编码方式的差异。我们的存储库为任何旨在从医学成像数据中提取采集细节的工具提供验证数据集。