医学影像AI集成服务器

# Clone and set up development environment
gh repo clone sscotti/dicom-mcp
cd dicom-mcp

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate

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

# DICOM nodes configuration
nodes:
  main:
    host: "localhost"
    port: 4242
    ae_title: "ORTHANC"
    description: "Local Orthanc DICOM server (Primary)"
  
  secondary:
    host: "localhost"
    port: 4243
    ae_title: "ORTHANC2"
    description: "Local Orthanc DICOM server (Secondary)"

current_node: "main"
calling_aet: "MCPSCU"

# FHIR server configuration (optional)
# You can configure multiple FHIR servers and switch between them
fhir_servers:
  firely:
    base_url: "https://server.fire.ly"
    description: "Firely FHIR Test Server (public, no API key needed)"
  
  siim:
    base_url: "https://hackathon.siim.org/fhir"
    api_key: "${SIIM_API_KEY}"  # Set in .env file
    description: "SIIM Hackathon FHIR server"
  
  # Uncomment to use a local HAPI FHIR server
  hapi_local:
    base_url: "http://localhost:8080/fhir"
    description: "Local HAPI FHIR server"

current_fhir: "hapi_local"  # Active FHIR server: firely, siim, or hapi_local, make sure to start the local hapi fhir server before starting the MCP server

# The server will expose all DICOM tools and FHIR tools via standard MCP protocol

# Mini-RIS MySQL database configuration (optional)
mini_ris:
  host: "localhost"
  port: 3306
  user: "orthanc_ris_app"
  password: "${MINI_RIS_DB_PASSWORD}"
  database: "orthanc_ris"
  pool_size: 5

docker-compose up -d
dotenv run -- pytest # uploads dummy pdf data to ORTHANC server

# Navigate to your dicom-mcp directory
cd /path/to/dicom-mcp

# Activate your virtual environment
source venv/bin/activate

# Start MCP Jam (use latest or beta)
npx -y @mcpjam/inspector@latest
# or
npx -y @mcpjam/inspector@beta

MINI_RIS_DB_PASSWORD=orthanc_ris_app  # Default password for orthanc_ris_app user

# Example: Patient arrives, technician creates MWL from order
# In MCP Jam or via LLM:
create_mwl_from_order(
    order_id=1,
    scheduled_station_aet="ORTHANC"
)

# Returns:
{
  "success": true,
  "message": "MWL created successfully for order 1",
  "accession_number": "ACC-2025-0001",
  "patient_name": "Alex Johnson",
  "patient_id": "MRN1001",
  "procedure": "Chest X-Ray 2 Views",
  "modality": "CR",
  "scheduled_time": "2025-06-01 09:15:00",
  "mwl_id": 42
}

# Example: Complete imaging workflow
# 1. Create MWL from order
create_mwl_from_order(order_id=1)

# 2. Simulate CR device acquiring images
create_synthetic_cr_study(
    accession_number="ACC-2025-0001",
    image_mode="simple",  # or "ai" with OpenAI key
    send_to_pacs=True
)

# Result: 2 CR images created and sent to Orthanc!

OPENAI_API_KEY=sk-proj-xxxxx  # Optional - enables AI-generated images

User: "Patient Johnson completed their chest x-ray, create the study"
LLM: Calls create_synthetic_cr_study(accession_number="MCP-ACC-2025-0001", image_mode="simple")
Result: 2-view chest study appears in Orthanc instantly!

User: "Generate a realistic chest x-ray showing pneumonia in the right lung"
LLM: Calls with image_mode="ai", image_description="pneumonia right lower lobe"
Result: gpt-image-1 generates photorealistic pneumonia appearance (~40 seconds)
Note: May show timeout error but images still arrive in PACS

# Example: Order with prompts
# Order MCP-ORD-2025-0001 has:
#   image_generation_prompt: "Chest X-ray showing subtle patchy opacity in right lower lobe"
#   report_findings_description: "Subtle patchy opacity in right lower lobe consistent with early pneumonia"

# 1. Generate images using order prompt (automatically used if available)
create_synthetic_cr_study(
    accession_number="MCP-ACC-2025-0001",
    image_mode="ai"  # Will use order's image_generation_prompt
)

# 2. Create report using order findings (optional)
create_radiology_report(
    accession_number="MCP-ACC-2025-0001",
    findings="",  # Empty - will use order's report_findings_description
    impression="Early pneumonia",
    use_order_findings=True  # Use order's report_findings_description
)

Order → MWL → Virtual Device → DICOM Images → PACS Storage → Viewing → Reporting

# Complete reporting workflow
# 1. Get study information for reporting
study_info = get_study_for_report(accession_number="MCP-ACC-2025-0001")

# 2. List available radiologists
radiologists = list_radiologists()

# 3. Create a radiology report
# Option A: Manual findings
report = create_radiology_report(
    accession_number="MCP-ACC-2025-0001",
    findings="""
    The heart is normal in size. The mediastinum is unremarkable.
    Both lungs are clear without focal consolidation, pleural effusion, or pneumothorax.
    No acute bony abnormality is identified.
    """,
    impression="Normal chest radiograph. No acute cardiopulmonary process.",
    author_provider_id=3,  # Dr. MCP-Casey Wells (from list_radiologists)
    report_status="Final"
)

# Option B: Use order's report_findings_description (if available)
report = create_radiology_report(
    accession_number="MCP-ACC-2025-0001",
    findings="",  # Empty - will use order's report_findings_description
    impression="Early pneumonia",
    author_provider_id=3,
    report_status="Final",
    use_order_findings=True  # Use order's report_findings_description
)

# 4. Generate PDF preview (optional)
pdf_data = generate_report_pdf(report_id=report['report_id'])
# Returns base64 encoded PDF for preview/download

# 5. Attach report to PACS as DICOM Encapsulated PDF
result = attach_report_to_pacs(report_id=report['report_id'])
# Report PDF now appears as a DOC series in Orthanc!

User: "Create a final report for accession ACC-2025-0001. Normal chest x-ray."
LLM: → get_study_for_report() to fetch patient/study data
     → list_radiologists() to get available radiologists
     → create_radiology_report() with structured findings/impression
     → attach_report_to_pacs() to send PDF to PACS
Result: Complete report in database + PDF in PACS!

User: "Generate a preliminary report for the knee study showing a tibial fracture"
LLM: → Creates report with report_status="Preliminary"
     → Structures findings describing the fracture
     → Generates and attaches PDF to PACS
Result: Preliminary report available for review

User: "Show me the PDF for report ID 5"
LLM: → generate_report_pdf(report_id=5)
     → Returns base64 PDF for display/download

Preliminary → Final → [Amended] → [Cancelled]

reports:
  - report_id (PK)
  - imaging_study_id (FK to imaging_studies)
  - report_number (unique, e.g., "RPT-ACC-2025-0001-20250601120000")
  - author_provider_id (FK to providers - radiologist)
  - report_status (enum: Preliminary, Final, Amended, Cancelled)
  - report_datetime (timestamp)
  - report_text (LONGTEXT - findings)
  - impression (TEXT - clinical impression)
  - dicom_sop_instance_uid (populated after PACS attachment)
  - dicom_series_instance_uid (populated after PACS attachment)

┌─────────────────────────────────────────────────────────┐
│ 1. Order Management (Mini-RIS)                          │
│    create_mwl_from_order(order_id=1)                    │
└────────────────┬────────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────────┐
│ 2. Image Acquisition (Virtual CR Device)                │
│    create_synthetic_cr_study(accession="ACC-2025-0001") │
└────────────────┬────────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────────┐
│ 3. PACS Storage (Orthanc)                               │
│    Images viewable at http://localhost:8042             │
└────────────────┬────────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────────┐
│ 4. Radiology Reporting                                  │
│    create_radiology_report(...)                         │
│    attach_report_to_pacs(report_id=1)                   │
└────────────────┬────────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────────┐
│ 5. Complete Study in PACS                               │
│    - CR Images (Series 1, 2)                            │
│    - Report PDF (Series 9999, Modality DOC)             │
└─────────────────────────────────────────────────────────┘

# Populate synthetic data
python tests/populate_synthetic_fhir_data.py

# Activate virtual environment
source venv/bin/activate

# Start the custom web UI, experimental !!
python start_web_ui.py

# Or directly with uvicorn
uvicorn dicom_mcp.web_ui:app --host 127.0.0.1 --port 8080

export OPENAI_API_KEY="your-api-key-here"