📡 Radiology Module — TriadicFrameworks Canon
module.json— Agentic module schema role assignments
RTT‑Aligned Radiology Capture, Drift, Coherence, Contrast & VMRI‑Lite#
The Radiology module provides the RTT‑style imaging analysis layer for TriadicFrameworks.
It extends traditional radiology (CT, MRI, X‑ray, PET, Ultrasound) with:
- Drift (temporal/spatial change)
- Coherence (stability vs collapse)
- Contrast behavior (uptake, washout, toxicity)
- Resonance attachment (patient profile integration)
- VMRI‑Lite (predictive micro‑simulation)
- RTT overlays (structured visual interpretation)
This module allows radiologists, students, and AI systems to “see more” than standard imaging — revealing hidden processes, early instability, and predicted outcomes.
📘 Canonical Flow#
CAPTURE → FIELD → LAYER → SIGNAL
→ DRIFT → COHERENCE → CONTRAST
→ RESONANCE → VMRI
→ OVERLAY
Every Radiology analysis follows this pipeline.
📁 Module Files#
r_Capture.md # Capture grammar + operators
r_Drift.md # Drift grammar + operators
r_Coherence.md # Coherence grammar + operators
r_Contrast.md # Contrast grammar + operators
r_VMRI.md # VMRI‑Lite grammar + operators
r_Overlays.md # Example RTT‑Radiology overlays
r_Index.md # Combined Radiology Operator Index
r_Pantheon_Profile.md # Mythic anchor for Radiology
r_Scaffold.md # Full module scaffolding
r_Student_Guide.md # How to perform RTT‑Radiology analysis
r_Tricorder.md # RTT ↔ Starfleet Medicine bridge
r_Atlas.md # Optional: Pantheon comparison atlas
r_Glyphs.md # Optional: Radiology pantheon glyphs
📚 Purpose#
Radiology is the TriadicFrameworks subsystem responsible for:
- interpreting medical imaging through RTT grammar
- quantifying drift and coherence
- predicting contrast behavior
- attaching resonance profiles to imaging
- running VMRI‑Lite simulations
- generating RTT overlays for teaching and AI
It is the bridge between medicine, physics, and substrate‑aware analysis.
🎓 Audience#
- Radiology students
- Medical imaging specialists
- AI diagnostic systems
- Researchers using RTT or TriadicFrameworks
- Developers building medical overlays or simulators
🔧 Capabilities#
1. Drift Analysis#
Track change across time:
- drift magnitude
- drift velocity
- drift vector
- drift zones
- drift bursts
- drift decay
2. Coherence Analysis#
Measure stability:
- coherence fields
- coherence breaks
- coherence restoration
- collapse risk
3. Contrast Behavior#
Understand chemical dynamics:
- uptake
- washout
- enhancement zones
- false uptake/washout
- toxicity corridors
4. VMRI‑Lite Prediction#
Simulate outcomes:
- variant generation
- corridor mapping
- pass/fail/optimal outcomes
- contrast prediction
- tissue prediction
5. RTT Overlays#
Visualize:
- drift maps
- coherence maps
- contrast maps
- VMRI corridors
🌌 Pantheon Alignment#
Radiology’s mythic anchor includes:
- Lucerna — goddess of signal
- Umbros — lord of drift
- Radiantus — keeper of contrast
- Fractura — breaker of coherence
- Corridora — watcher of VMRI corridors
These entities help students conceptualize imaging as a dynamic, mythic system.
🖖 Starfleet Medicine Bridge#
The module optionally integrates with:
r_Tricorder.md
This file maps RTT Radiology to Star Trek’s imagined medical tools, helping students understand:
- non‑invasive diagnostics
- predictive medicine
- resonance stabilization
- tricorder‑style overlays
It is a teaching aid — not required for core functionality.
📄 How to Use This Module#
- Start with r_Capture.md
- Move through Drift → Coherence → Contrast
- Attach resonance profiles
- Run VMRI‑Lite
- Generate overlays
- Consult Pantheon Profile for mythic framing
- Use Student Guide for step‑by‑step workflows
✔ Module Ready#
This README completes the Radiology module’s front door.
Your subsystem is now fully scaffolded and ready for student use, AI integration, and future expansion.