Dicom Mcp

What is Dicom Mcp about?

Dicom Mcp provides a Model Context Protocol (MCP) server that exposes a set of tools for interacting with DICOM nodes. It lets downstream agents search patient, study, series, and instance metadata, extract text from encapsulated PDF reports, and move whole series or studies to other DICOM destinations (e.g., AI segmentation services).

How to use Dicom Mcp?

1. Install – uv tool install dicom-mcp or pip install dicom-mcp.
2. Create a configuration file (YAML) defining DICOM nodes, the calling AE title, and the active node.
3. Start the server – run the MCP server with the config file, e.g., npx dicom-mcp /path/to/config.yaml.
4. Call tools – from an LLM‑agent or any client, invoke the exposed tools (query_patients, move_series, etc.) via the MCP transport (stdio, HTTP, etc.).

Key features of Dicom Mcp

• Metadata query: query_patients, query_studies, query_series, query_instances with flexible criteria.
• PDF report extraction: extract_pdf_text_from_dicom returns plain‑text from encapsulated PDFs.
• DICOM C‑MOVE support: move_series and move_study forward images to configured remote nodes.
• Utility commands: list/switch nodes, verify connections, and view attribute presets.
• Python‑centric implementation built on pynetdicom and PyPDF2.

Use cases of Dicom Mcp

• Clinical decision support: An LLM asks for the latest radiology report, receives extracted text, and provides insights.
• Automated image analysis pipelines: Move a CT series to an AI segmentation node, retrieve the generated volume report, and summarize findings.
• Research data preparation: Batch‑query studies of a specific modality and export metadata for statistical analysis.
• Testing & education: Spin up a local Orthanc server with Docker, upload dummy data, and experiment with DICOM interactions safely.

FAQ from the Dicom Mcp

• Q: Do I need a live hospital PACS? A: No. A local Orthanc instance (Docker‑compose) works for development and testing.
• Q: Is the server safe for production use? A: It is intended for research and prototype environments. Do not connect to sensitive clinical systems without proper security review.
• Q: Which Python versions are supported? A: Python 3.12+ (as indicated by the badge).
• Q: How do I run tests? A: Start the Orthanc container (docker-compose up -d in the tests folder) and execute pytest from the project root.
• Q: Can I invoke the server from a Node.js client? A: Yes. Use the MCP transport (e.g., stdio) and start the server with npx dicom-mcp ….

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