DICOM MCP Server for Medical Imaging Systems 🏥

Name: DICOM MCP Server
Author: Y5ive9ine

License: MIT Python Version PyPI Version PyPI Downloads

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

--------------------------------------------------------------------- 🧑‍⚕️ 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.
📥 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.

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

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"])

DICOM MCP Server