DICOM MCP Server

DICOM MCP Server for Medical Imaging Systems 🏥

The dicom-mcp server enables AI assistants to query, read, and move data on DICOM servers (PACS, VNA, etc.).

🤝 Contribute • 📝 Report Bug • 📝 Blog Post 1

---------------------------------------------------------------------
🧑‍⚕️ User: "Any significant findings in John Doe's previous CT report?"

🧠 LLM → ⚙️ Tools:
   query_patients → query_studies → query_series → extract_pdf_text_from_dicom

💬 LLM Response: "The report from 2025-03-26 mentions a history of splenomegaly (enlarged spleen)"

🧑‍⚕️ User: "What's the volume of his spleen at the last scan and the scan today?"

🧠 LLM → ⚙️ Tools:
   (query_studies → query_series → move_series → query_series → extract_pdf_text_from_dicom) x2
   (The move_series tool sends the latest CT to a DICOM segmentation node, which returns volume PDF report)

💬 LLM Response: "last year 2024-03-26: 412cm³, today 2025-04-10: 350cm³"
---------------------------------------------------------------------

✨ Core Capabilities

dicom-mcp provides tools to:

🔍 Query Metadata: Search for patients, studies, series, and instances using various criteria.
📄 Read DICOM Reports (PDF): Retrieve DICOM instances containing encapsulated PDFs (e.g., clinical reports) and extract the text content.
➡️ Send DICOM Images: Send series or studies to other DICOM destinations, e.g. AI endpoints for image segmentation, classification, etc.
⚙️ Utilities: Manage connections and understand query options.

🚀 Quick Start

📥 Installation

Install using uv or pip:

uv tool install dicom-mcp

Or by cloning the repository:

# Clone and set up development environment
git clone https://github.com/ChristianHinge/dicom-mcp
cd dicom mcp

# Create and activate virtual environment
uv venv
source .venv/bin/activate

# Install with test dependencies
uv pip install -e ".[dev]"

⚙️ Configuration

dicom-mcp requires a YAML configuration file (config.yaml or similar) defining DICOM nodes and calling AE titles. Adapt the configuration or keep as is for compatibility with the sample ORTHANC Server.

nodes:
  main:
    host: "localhost"
    port: 4242 
    ae_title: "ORTHANC"
    description: "Local Orthanc DICOM server"

current_node: "main"
calling_aet: "MCPSCU" 

Warning

DICOM-MCP is not meant for clinical use, and should not be connected with live hospital databases or databases with patient-sensitive data. Doing so could lead to both loss of patient data, and leakage of patient data onto the internet. DICOM-MCP can be used with locally hosted open-weight LLMs for complete data privacy.

(Optional) Sample ORTHANC server

If you don't have a DICOM server available, you can run a local ORTHANC server using Docker:

Clone the repository and install test dependencies pip install -e ".[dev]

cd tests
docker ocmpose up -d
cd ..
pytest # uploads dummy pdf data to ORTHANC server

UI at http://localhost:8042

🔌 MCP Integration

Add to your client configuration (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "dicom": {
      "command": "uv",
      "args": ["tool","dicom-mcp", "/path/to/your_config.yaml"]
    }
  }
}

For development:

{
    "mcpServers": {
        "arxiv-mcp-server": {
            "command": "uv",
            "args": [
                "--directory",
                "path/to/cloned/dicom-mcp",
                "run",
                "dicom-mcp",
                "/path/to/your_config.yaml"
            ]
        }
    }
}

🛠️ Tools Overview

dicom-mcp provides four categories of tools for interaction with DICOM servers and DICOM data.

🔍 Query Metadata

query_patients: Search for patients based on criteria like name, ID, or birth date.
query_studies: Find studies using patient ID, date, modality, description, accession number, or Study UID.
query_series: Locate series within a specific study using modality, series number/description, or Series UID.
query_instances: Find individual instances (images/objects) within a series using instance number or SOP Instance UID

📄 Read DICOM Reports (PDF)

extract_pdf_text_from_dicom: Retrieve a specific DICOM instance containing an encapsulated PDF and extract its text content.

➡️ Send DICOM Images

move_series: Send a specific DICOM series to another configured DICOM node using C-MOVE.
move_study: Send an entire DICOM study to another configured DICOM node using C-MOVE.

⚙️ Utilities

list_dicom_nodes: Show the currently active DICOM node and list all configured nodes.
switch_dicom_node: Change the active DICOM node for subsequent operations.
verify_connection: Test the DICOM network connection to the currently active node using C-ECHO.
get_attribute_presets: List the available levels of detail (minimal, standard, extended) for metadata query results.

Example interaction

The tools can be chained together to answer complex questions:

📈 Contributing

Running Tests

Tests require a running Orthanc DICOM server. You can use Docker:

# Navigate to the directory containing docker-compose.yml (e.g., tests/)
cd tests
docker-compose up -d

Run tests using pytest:

# From the project root directory
pytest

Stop the Orthanc container:

cd tests
docker-compose down

Debugging

Use the MCP Inspector for debugging the server communication:

npx @modelcontextprotocol/inspector uv run dicom-mcp /path/to/your_config.yaml --transport stdio

🙏 Acknowledgments

Built using pynetdicom
Uses PyPDF2 for PDF text extraction

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Ermöglicht KI-Assistenten, medizinische Bildmetadaten von DICOM-Servern abzufragen und zu analysieren, einschließlich Patienteninformationen, Studien, Serien und Instanzen, sowie Text aus gekapselten PDF-Dokumenten zu extrahieren.

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