🛂 EDRN DICOM Validation

A validation tool for DICOM files used by the Laboratory Catalog and Archive Service (LabCAS) of the Early Detection Research Network (EDRN). This program ensures that DICOM files:

• Contain little-to-no PHI/PII — Scans both DICOM headers and pixel data for protected health information (PHI) and personally identifiable information (PII)
• Adhere to EDRN requirements — Validates DICOM tags against the EDRN core and MR requirements

This tool was developed in response to EDRN/EDRN-metadata#160.

🎯 Features

This program has features described in the following subsections.

🔍 PHI/PII Detection

• Header-based detection: Scans DICOM metadata tags for identifiers including:
  • Patient names, birth dates, addresses
  • Physician and operator names
  • Email addresses, phone numbers, SSNs
  • Medical record numbers (MRNs)
• Pixel-based detection: Uses OCR (Tesseract) to detect text embedded in DICOM images
• Multiple recognizers: Choose between different PHI/PII detection algorithms:
  • simple-scoring (default): Pattern-based detection with configurable scoring
  • accepting: Accepts all files (testing only)
  • rejecting: Rejects all files (testing only)

✅ DICOM Tag Validation

Validates over 40 DICOM tags against EDRN requirements including:

• Study/Series/Image Identification: UIDs, instance numbers, SOP class
• Acquisition Modality and Equipment: Modality codes, manufacturer info, device details
• Temporal Data: Dates and times in proper format
• Image Data: Dimensions, pixel data, display parameters
• MR-specific: Spacing between slices validation

📊 Reporting

Generates detailed Markdown reports organized by:

• Site ID
• Event ID
• File name
• Finding type and severity score

📦 Installation

Details on installing this software follows in this section.

⚙️ Prerequisites

Requires Python 3.12 or higher and Tesseract OCR for pixel-based PHI/PII detection.

🔤 Tesseract

Tesseract provides optical character recgonition features for this program and must be installed separately.

macOS:

brew install tesseract

Linux (Ubuntu/Debian):

sudo apt-get install tesseract-ocr

Windows: Download from https://github.com/UB-Mannheim/tesseract/wiki

📥 Install the Package

It's best to set up a Python virtual environment and use pip to install it into that environment:

pip install jpl.labcas.validation

Or install from source:

git clone https://github.com/EDRN/jpl.labcas.validation.git
cd jpl.labcas.validation
pip install --editable .

🚀 Usage

The following describes how to use this program.

💻 Basic Usage

The easiest way to run this is:

validate-dicom-files <directory>

the <directory> should eventually contain the following directory hierarchy:

<directory>
    … (sub-directories)
    collection-folder (such as Prostate_MRI)
        event-ID-folder (such as 1234567)
        … (sub-folders)
            DICOM file 1
            DICOM file 2
            …

⚡ Command-Line Options

Use --help to get more details, but summarizing:

• -s, --score <value>: Maximum PHI/PII score threshold (0.0-1.0, default: 0.8)
• -c, --concurrency <num>: Number of concurrent processes (default: CPU count)
• -r, --recognizer <name>: PHI/PII recognizer to use:
  • simple-scoring (default): Pattern-based detection
  • accepting: Accept all files
  • rejecting: Reject all files
• -o, --output <file>: Output file for report (default: report.md)
• -v, --verbose: Verbose logging
• -q, --quiet: Quiet logging

📝 Examples

Validate a directory with default settings:

validate-dicom-files /path/to/dicom/files

Use a different PHI/PII threshold (lower = less strict):

validate-dicom-files --score 0.5 /path/to/dicom/files

Generate a custom report filename:

validate-dicom-files --output validation_results.md /path/to/dicom/files

Use a specific number of workers:

validate-dicom-files --concurrency 4 /path/to/dicom/files

In general, use a --concurrency equal to at least the number of CPU cores available. Some recommend using twice that number.

📖 Understanding the Report

The tool generates a Markdown report with findings organized hierarchically:

1. By Site ID: Grouped by blinded site identifier
2. By Event ID: Grouped by 7-digit event ID
3. By File: Individual DICOM files within each event
4. By Finding: Each finding includes:
   • Score: Severity from 0.0 (low) to 1.0 (high)
   • Kind: Type of finding:
     • 🙈 Header: PHI/PII found in DICOM metadata
     • 🖼️ Pixels: PHI/PII found in image data via OCR
     • ⚠️ Validation: Tag compliance issue
     • ❌ Error: File reading or processing error
   • Details: Specific information about the finding

Only findings with scores above the threshold are included in the report.

🏗️ Architecture

The validation framework is modular and extensible:

• PHI/PII Recognizers: Plug-in system for different detection algorithms
• Validators: Individual validators for each DICOM tag requirement
• Findings: Structured representation of all issues discovered

🧪 Development Status

Development Status: Pre-Alpha

CT requirements may be added in the future, pending completion of the spreadsheet's CT tab.

📄 License

Apache 2.0 - See LICENSE.md for details

🤝 Contributing

Issues and pull requests welcome on GitHub: https://github.com/EDRN/jpl.labcas.validation/issues. See also the EDRN Code of Conduct and Contributors' Guide.

👤 Authors

• Sean Kelly @nutjob4life

©️ Copyright

Copyright © 2025 California Institute of Technology. U.S. Government sponsorship acknowledged.