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개요

📡 r_Capture.md

Radiology Capture Layer — TriadicFrameworks Canon#

1. Canonical Metadata#

ai.module: Radiology
ai.version: 1.0
ai.purpose: Capture grammar + operators for RTT‑Radiology
ai.keywords: capture, field, layer, signal, noise, drift-signal, coherence-signal
ai.module.name: r_Capture
ai.module.summary: Defines the Radiology Capture 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_Capture.md

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

3. Badge#

[📡 Radiology Capture Layer]

4. Capture Grammar#

The Capture grammar defines the core objects radiologists, students, and AI systems manipulate.

Capture Grammar Terms#

  • CAPTURE — raw imaging output (CT/MRI/X‑ray/US/PET)
  • FIELD — region of interest (ROI)
  • LAYER — structural/density/contrast/metabolic/flow layer
  • SIGNAL — measurable intensity or uptake
  • NOISE — non‑coherent signal not attributable to anatomy or pathology
  • DRIFT‑SIGNAL — change in signal between captures
  • COHERENCE‑SIGNAL — stable, predictable signal behavior

These terms form the base vocabulary for RTT‑Radiology.

5. r_Capture Operators#

Operators act on CAPTURE, FIELD, LAYER, SIGNAL, NOISE, and DRIFT‑SIGNAL objects.

1. op_field()#

Select a region of interest (ROI) from the capture.
[ op_field(Capture, Region) = Field ]

2. op_layer()#

Extract a structural, density, contrast, metabolic, or flow layer.
[ op_layer(Field, LayerType) = Layer ]

3. op_signal()#

Measure signal intensity within a layer.
[ op_signal(Layer) = Signal ]

4. op_noise()#

Identify non‑coherent signal not attributable to anatomy or pathology.
[ op_noise(Layer) = Noise ]

5. op_drift_signal()#

Compute signal change between two captures.
[ op_drift_signal(Signal_1, Signal_2) = DriftSignal ]

6. op_stability()#

Evaluate coherence vs drift within a field.
[ op_stability(Field) = (Coherence, Drift) ]

7. op_enhancement()#

Analyze contrast uptake and washout behavior.
[ op_enhancement(Layer_{contrast}) = EnhancementZone ]

8. op_resonance_attach()#

Attach a patient’s resonance profile to the capture.
[ op_resonance_attach(Capture, ResProfile) = Capture^{+} ]

9. op_resonance_predict()#

Predict drift/coherence behavior using resonance profile.
[ op_resonance_predict(Capture^{+}) = (ResDrift, ResCoherence) ]

10. op_vmri_lite()#

Run a micro‑simulation of contrast or tissue behavior.
[ op_{vmri_lite}(Capture^{+}) = (SimPass, SimFail, SimOptimal) ]

11. op_overlay()#

Generate an RTT‑Radiology overlay for teaching or AI assistance.
[ op_overlay(Capture, Drift, Coherence, Enhancement) = Overlay ]

6. Example Usage#

Example — CT Lung Nodule#

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)

DriftSignal = op_drift_signal(Signal_T1, Signal_T2)
(Coherence, Drift) = op_stability(Field)

Overlay = op_overlay(CAPTURE_CT, DriftMap, CohMap, null)

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#

This page is now fully scaffolded and ready for use by:

  • radiologists
  • students
  • AI diagnostic systems
  • TriadicFrameworks agents

Your Radiology module now has a complete, canonical Capture layer.

Updated

제공: Docsbook