Ror Workflow

ROR Workflow Tool

This skill provides guidance for working with ROR (Research-Information-System), a workflow tool for research PACS that enables processing pipelines for medical imaging data.

When to Use This Skill

Use this skill when:

• Exploring DICOM datasets (listing patients, studies, series)
• Setting up data processing workflows for medical imaging
• Filtering DICOM series by modality, description, or other metadata
• Querying specific series for batch processing pipelines
• Working with research PACS data organization

Available MCP Tools

ROR provides the following MCP tools for data interaction:

Project Management

Data Exploration

Series Selection

MCP Resources

Access these resources via ReadMcpResourceTool with server ror_mcp:

Common Workflows

Workflow 1: Initial Data Exploration

To explore a new DICOM dataset:

1. Initialize project (if needed):

   mcp__ror_mcp__project_init(path="/path/to/project")
   

2. Add data folder:

   mcp__ror_mcp__data_add(path="/path/to/dicom/data")
   

   Wait for parsing to complete before proceeding.

3. Get overview:

   • List patients: mcp__ror_mcp__get_patients_info()
   • Get studies for a patient: mcp__ror_mcp__get_study_info(name="PatientName")
   • Get all studies: mcp__ror_mcp__get_study_info(name="")

4. Drill down to series:

   • Get series for a study: mcp__ror_mcp__get_series_info(study_instance_uid="...")
   • Examine series tags: mcp__ror_mcp__get_series_tags(series_instance_uid="...")

Workflow 2: Setting Up Series Selection

To filter DICOM series for processing:

1. Understand the data by exploring patients, studies, and series first

2. Write a SELECT statement using the query syntax (see references/select-syntax.md):

   SELECT series FROM study
   WHERE series NAMED "T1" HAS
     Modality == 'MR' AND SeriesDescription regexp 'T1'
   

3. Set the filter:

   mcp__ror_mcp__set_new_select_statement(select="SELECT series FROM study WHERE ...")
   

4. Verify the selection:

   mcp__ror_mcp__get_current_select_statement()
   

Workflow 3: Multi-Series Workflow Setup

To select multiple related series (e.g., T1 + FLAIR + DWI for analysis):

SELECT patient FROM study
WHERE series NAMED "T1" HAS
  ClassifyType containing T1
ALSO WHERE series NAMED "FLAIR" HAS
  SeriesDescription regexp 'FLAIR'
ALSO WHERE series NAMED "DWI" HAS
  ClassifyType containing DIFFUSION

This returns patients that have all three series types available.

SELECT Statement Quick Reference

Basic syntax:

SELECT <level> FROM study WHERE series [NAMED "name"] HAS <conditions>

Output levels: patient, study, series

Operators:

• ==, containing - exact match
• regexp - regular expression
• >, <, >=, <= - numeric comparison
• AND, OR, NOT() - logical operators

Common DICOM fields:

• Modality - MR, CT, US, etc.
• SeriesDescription - Series name text
• NumImages - Image count
• ClassifyType - Custom classification

For complete syntax reference, read the references/select-syntax.md file.

Best Practices

Data Exploration

• Always explore the data structure before writing SELECT statements
• Use get_series_tags to discover available metadata fields
• Start with simple queries and refine iteratively

SELECT Statements

• Use NAMED to label series for later reference in workflows
• Use regexp for flexible pattern matching on descriptions
• Use ALSO WHERE to require multiple series types per patient/study

Performance

• Wait for data_add parsing to complete before querying
• Use specific patient names rather than empty string for large datasets
• Filter at the most specific level needed (series vs study vs patient)

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