ror-workflow

name	ror-workflow
description	Work with ROR (Research-Information-System) for DICOM data exploration, workflow setup, and series selection in research PACS environments. This skill should be used when exploring medical imaging datasets, setting up processing workflows, querying DICOM series/studies by modality or metadata, or filtering patients/studies for batch processing.

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

Tool	Purpose
mcp__ror_mcp__project_init	Initialize a new ROR project folder
mcp__ror_mcp__data_add	Add a DICOM data folder (triggers parsing)
mcp__ror_mcp__data_clear	Clear all imported data references
mcp__ror_mcp__roots	Manage ROR roots configuration
mcp__ror_mcp__roots_list	List currently configured roots
mcp__ror_mcp__ror_info	Get ROR tool information

Data Exploration

Tool	Purpose
mcp__ror_mcp__get_patients_info	List all patients/participants
mcp__ror_mcp__get_study_info	Get studies for a patient (or all if empty name)
mcp__ror_mcp__get_series_info	Get series for a study (requires StudyInstanceUID)
mcp__ror_mcp__get_series_tags	Get DICOM tags for a specific series

Series Selection

Tool	Purpose
mcp__ror_mcp__get_current_select_statement	View current filter query
mcp__ror_mcp__set_new_select_statement	Set new SELECT filter

MCP Resources

Access these resources via ReadMcpResourceTool with server ror_mcp:

Resource URI	Description
embedded:info	ROR tool information
embedded:data	Current data summary (JSON)
embedded:numparticipants	Count of participants
embedded:numstudies	Count of studies
embedded:numseries	Count of series
embedded:numimages	Count of images
embedded:tag	Get DICOM tag name from group/element
embedded:tagname	Get DICOM tag group/element from name

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)