DICOM datasets for reproducible neuroimaging research across manufacturers and software versions

TL;DR

This paper introduces DICOM datasets to help neuroimaging researchers accurately extract acquisition details across different manufacturers and software versions.

Contribution

The paper provides standardized test datasets with NIfTI and BIDS metadata to improve reproducibility in neuroimaging.

Findings

DICOM datasets demonstrate how manufacturers encode acquisition details using public and private tags.

NIfTI and BIDS metadata help mitigate inconsistencies in manufacturer-specific data encoding.

The repository serves as a validation resource for tools extracting imaging metadata.

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.

Figures