DICOM Anonymizer

For research purposes, selecting an open-source DICOM anonymizer involves balancing ease of use with the need to protect patient privacy by removing Protected Health Information (PHI) from DICOM tags and pixel data.

Here are the best open-source or free DICOM anonymizer tools for research based on common use cases:

RSNA DICOM Anonymizer: A highly regarded, Python-based, open-source tool from the Radiological Society of North America designed specifically for research. It is highly configurable, allowing users to choose the level of anonymization needed and can export files to cloud storage.

DicomBrowser: A popular tool for visualizing and editing DICOM tags, allowing for manual or script-based anonymization by removing or changing personal identifiers.

Dicompyler: A free, open-source DICOM viewer that includes an anonymization module, specifically useful for radiation oncology research.

Pydicom: A Python package that allows developers to write custom scripts to alter DICOM tag values, providing maximum flexibility for automated, script-based anonymization workflows.

XNAT: A comprehensive imaging informatics platform (open-source) that includes robust tools for managing and anonymizing DICOM data as part of research pipelines. Key Features for Research Anonymizers:

Tag Defacing/Modification: Ability to edit or remove sensitive fields like patient name, ID, and date of birth.

Pixel Data Cleaning: Capability to detect and remove PHI “burned” into the image pixels themselves.

Workflow Automation: Tools that can be scripted to process large datasets.

When choosing, it’s important to verify that the tool handles both standard DICOM tags and private vendor-specific tags that may contain PHI. To provide you with the most relevant options, A scripting tool to process massive datasets?

A pipeline tool that integrates with a PACS (like XNAT or DCM4CHEE)? Imaging research tools – RSNA