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Overview

📡 r_Capture.md

Radiology Capture Layer — TriadicFrameworks Canon#

The Capture layer defines the foundational grammar and operators used to interpret any radiological imaging modality (CT, MRI, X‑ray, PET, Ultrasound).
It is the “front door” of RTT‑Radiology.

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.

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