iflow-mcp_christianhinge-dicom-mcp 0.1.1
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Requires Python: >=3.11

# DICOM MCP Server for Medical Imaging Systems 🏥

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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The `dicom-mcp` server enables AI assistants to query, read, and move data on DICOM servers (PACS, VNA, etc.). 

<div align="center">

🤝 **[Contribute](#contributing)** •
📝 **[Report Bug](https://github.com/ChristianHinge/dicom-mcp/issues)**  •
📝 **[Blog Post 1](https://www.christianhinge.com/projects/dicom-mcp/)** 

</div>

```text
---------------------------------------------------------------------
🧑‍⚕️ 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:

```bash
uv tool install dicom-mcp
```
Or by cloning the repository:

```bash
# 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.

```yaml
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]`

```bash
cd tests
docker ocmpose up -d
cd ..
pytest # uploads dummy pdf data to ORTHANC server
```
UI at [http://localhost:8042](http://localhost:8042)

### 🔌 MCP Integration

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

```json
{
  "mcpServers": {
    "dicom": {
      "command": "uvx",
      "args": ["dicom-mcp", "/path/to/your_config.yaml"]
    }
  }
}
```

For development:

```json
{
    "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.<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:

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

Run tests using pytest:

```bash
# From the project root directory
pytest
```

Stop the Orthanc container:

```bash
cd tests
docker-compose down
```

### Debugging

Use the MCP Inspector for debugging the server communication:

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

## 🙏 Acknowledgments

* Built using [pynetdicom](https://github.com/pydicom/pynetdicom)
* Uses [PyPDF2](https://pypi.org/project/PyPDF2/) for PDF text extraction