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.).

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🤝 Contribute • 📝 Report Bug •

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🧑‍⚕️ 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"
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✨ 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.
• 📥 Download DICOM Files: Download DICOM instances from the server to a local directory using C-GET. Retrieve entire series or specific instances for local analysis and processing.
• ➡️ 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/Y5ive9ine/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.

📥 Download DICOM Files

• retrieve_dicom_instances: Download DICOM instances from the server to a local directory using C-GET. Retrieve entire series or specific instances for local analysis and processing.

➡️ 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.<p>

Example interaction

The tools can be chained together to answer complex questions:

<div align="center"> <img src="images/example.png" alt="My Awesome Diagram" width="700"> </div>

📈 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

Usage Examples

Basic Patient Query

# Find all patients with name starting with "SMITH"
patients = query_patients(name_pattern="SMITH*")

Study Query with Date Range

# Find CT studies from January 2023
studies = query_studies(
    modality_in_study="CT", 
    study_date="20230101-20230131"
)

Download DICOM Files

# Download entire series to local directory
result = retrieve_dicom_instances(
    series_instance_uid="1.2.840.113619.2.1.1.322.1600364094.412.2005",
    output_directory="/path/to/local/dicom/files"
)

# Download specific instance only
result = retrieve_dicom_instances(
    series_instance_uid="1.2.840.113619.2.1.1.322.1600364094.412.2005",
    sop_instance_uid="1.2.840.113619.2.1.1.322.1600364094.412.3001",
    output_directory="/path/to/local/dicom/files"
)

print(f"Downloaded {result['total_files']} files ({result['total_size_mb']} MB)")
print(f"Files saved to: {result['output_directory']}")

# Files will be named with meaningful information like:
# "12345_SMITH_20230215_CT_CHEST_AXIAL_Inst001.dcm"
# "12345_SMITH_20230215_CT_CHEST_AXIAL_Inst002.dcm"

Extract PDF Reports

# Extract text from a DICOM PDF report
result = extract_pdf_text_from_dicom(
    study_instance_uid="1.2.840.113619.2.1.1.322.1600364094.412.1009",
    series_instance_uid="1.2.840.113619.2.1.1.322.1600364094.412.2005", 
    sop_instance_uid="1.2.840.113619.2.1.1.322.1600364094.412.3001"
)

if result["success"]:
    print("Report text:", result["text_content"])