DICOM datasets for reproducible neuroimaging research across manufacturers and software versions

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.

Abstract

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.

Citation

Rorden, C., Béranger, B., Cheng, H., Clemence, M., Debacker, C., Fernandez, B., Halchenko, Y. O., Harms, M. P., Holla, B., Innis, I., Kuijer, J. P. A., Levitas, D., Litinas, K., Luci, J., Newman-Norlund, R., Peltier, S., Rehwald, W., Reid, R. I., Rogers, B., Schwarz, C. G., …Morgan, P. S. (in press). DICOM datasets for reproducible neuroimaging research across manufacturers and software versions. Scientific Data, 12, Article 1168. https://doi.org/10.1038/s41597-025-05503-w

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2025
• Online Publication Date: Jul 9, 2025
• Deposit Date: Aug 8, 2025
• Publicly Available Date: Aug 8, 2025
• Journal: Scientific Data
• Electronic ISSN: 2052-4463
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Article Number: 1168
• DOI: https://doi.org/10.1038/s41597-025-05503-w
• Public URL: https://nottingham-repository.worktribe.com/output/52570080
• Publisher URL: https://www.nature.com/articles/s41597-025-05503-w#Abs1
• Additional Information: Received: 16 December 2024; Accepted: 1 July 2025; First Online: 9 July 2025; : Some authors of this manuscript are employed by imaging equipment manufacturers, specifically Philips (MC, SG), General Electric (BF, JS), and Siemens (WR). Their contributions were made in the interest of promoting transparency and reproducibility in scientific research. These individuals provided technical insights that facilitate the interpretation of vendor-specific attributes and support the broader community’s efforts to harmonize metadata extraction across manufacturers. No commercial products are promoted herein. These contributions are intended to encourage the alignment of vendor practices with evolving open standards, rather than maintain the status quo. The rest of the authors declare no competing interests.