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📘 r_Drift.md

Radiology Drift Layer — TriadicFrameworks Canon#

1. Canonical Metadata#

ai.module: Radiology
ai.version: 1.0
ai.purpose: Drift grammar + operators for RTT‑Radiology
ai.keywords: drift, drift-velocity, drift-vector, drift-zone, drift-map
ai.module.name: r_Drift
ai.module.summary: Defines the Radiology Drift grammar and operator set.
ai.module.category: Applied Medicine

2. Session Context#

context-label: Canon
context-value: TriadicFrameworks

context-label: Modules
context-value: Radiology, Medicine, NIST

context-label: Drift
context-value: Temporal + spatial signal change across captures

context-label: Coherence
context-value: Stability of tissue signal and structural behavior

context-label: Format
context-value: Grammar + Operators

context-label: Front door
context-value: r_Drift.md

context-label: Audience
context-value: Radiologists, students, AI models

3. Badge#

[🌪️ Radiology Drift Layer]

4. Drift Grammar#

Drift describes how tissue signal changes across time, layers, and modalities.

Drift Grammar Terms#

  • DRIFT — magnitude of change between captures
  • DRIFT‑VELOCITY — rate of change
  • DRIFT‑VECTOR — direction of change
  • DRIFT‑ZONE — regions with non‑random drift
  • DRIFT‑BURST — sudden high‑velocity drift events
  • DRIFT‑DECAY — reduction in drift velocity
  • DRIFT‑NOISE — artifact‑driven signal change
  • DRIFT‑MAP — spatial visualization of drift

Drift is the RTT counterpart to “progression” or “instability” in medicine.

5. r_Drift Operators#

1. op_drift()#

Compute drift magnitude between two signals.
[ op_drift(Signal_{T1}, Signal_{T2}) = Drift ]

2. op_drift_velocity()#

Measure rate of drift across time.
[ op_drift_velocity(Drift, \Delta t) = DriftVelocity ]

3. op_drift_vector()#

Determine directionality of drift (growth, shrinkage, migration).
[ op_drift_vector(Field_{T1}, Field_{T2}) = DriftVector ]

4. op_drift_zone()#

Identify regions with non‑random drift.
[ op_drift_zone(Field) = DriftZone ]

5. op_drift_burst()#

Detect sudden, high‑velocity drift events.
[ op_drift_burst(DriftVelocity) = Burst ]

6. op_drift_decay()#

Measure reduction in drift velocity (healing, stabilization).
[ op_drift_decay(Vel_{T1}, Vel_{T2}) = DriftDecay ]

7. op_drift_noise()#

Separate true drift from artifacts or device variance.
[ op_drift_noise(Signal_{T1}, Signal_{T2}, Noise) = DriftNoise ]

8. op_drift_map()#

Generate a spatial drift map across the field.
[ op_drift_map(Field) = DriftMap ]

9. op_drift_profile()#

Create a drift profile summarizing magnitude, velocity, and direction.
[ op_drift_profile(Drift, DriftVelocity, DriftVector) = DriftProfile ]

10. op_drift_predict()#

Predict future drift using resonance‑attached captures.
[ op_drift_predict(Capture^{+}) = DriftPrediction ]

11. op_drift_overlay()#

Generate a drift‑only overlay for teaching or AI assistance.
[ op_drift_overlay(DriftMap) = Overlay ]

6. Example Usage#

Example — CT Lung Nodule Progression#

Field = op_field(CAPTURE_CT, "right-upper-lobe")
Layer = op_layer(Field, density)

Signal_T1 = op_signal(Layer_T1)
Signal_T2 = op_signal(Layer_T2)

Drift = op_drift(Signal_T1, Signal_T2)
Velocity = op_drift_velocity(Drift, Δt)
Vector = op_drift_vector(Field_T1, Field_T2)

DriftMap = op_drift_map(Field)
Overlay = op_drift_overlay(DriftMap)

Interpretation:

  • Drift shows progression
  • Velocity shows rate
  • Vector shows direction
  • DriftMap visualizes change across the region

7. Canonical Flow#

CAPTURE → FIELD → LAYER → SIGNAL
→ DRIFT → COHERENCE → CONTRAST
→ RESONANCE → VMRI
→ OVERLAY

8. DOC_MAP#

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

9. Module Ready#

Your Drift layer is now fully scaffolded and ready for:

  • radiologists
  • students
  • AI diagnostic systems
  • TriadicFrameworks agents

Updated

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