niimath and fslmaths: replication as a method to enhance popular neuroimaging tools

Journal article

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

Authors: Rorden, C; Webster, M; Drake, C; Jenkinson, M; Clayden, JD

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Organization for Human Brain Mapping
• Journal: Aperture Neuro
• Volume: 4
• Publication date: 2024-03-08
• DOI: 10.52294/001c.94384
• ISSN: 2957-3963
• Language: English
• Keywords: Context (archaeology); Software portability; Programming language; Software; Human–computer interaction; Artificial intelligence; Plug-in; JavaScript; Executable; Replication (statistics); Computer science; World Wide Web