📘 Radiology Operator Index
TriadicFrameworks Canon — Complete Operator Reference#
- r_Capture
- r_Drift
- r_Coherence
- r_Contrast
- r_VMRI
It is designed for radiology students, medical AI systems, imaging researchers, and TriadicFrameworks module authors.
1. r_Capture Operators#
| Operator |
Purpose |
op_field() |
Select ROI from capture |
op_layer() |
Extract structural/density/contrast/metabolic/flow layer |
op_signal() |
Measure signal intensity |
op_noise() |
Identify non‑coherent signal |
op_drift_signal() |
Compute signal change between captures |
op_stability() |
Evaluate coherence vs drift |
op_enhancement() |
Analyze contrast uptake/washout |
op_resonance_attach() |
Attach resonance profile to capture |
op_resonance_predict() |
Predict drift/coherence behavior |
op_vmri_lite() |
Run micro‑simulation (VMRI‑Lite) |
op_overlay() |
Generate RTT‑Radiology overlay |
2. r_Drift Operators#
| Operator |
Purpose |
op_drift() |
Compute drift magnitude |
op_drift_velocity() |
Measure drift rate |
op_drift_vector() |
Determine drift direction |
op_drift_zone() |
Identify non‑random drift regions |
op_drift_burst() |
Detect sudden high‑velocity drift |
op_drift_decay() |
Measure reduction in drift velocity |
op_drift_noise() |
Separate drift from artifacts |
op_drift_map() |
Generate spatial drift map |
op_drift_profile() |
Summarize drift behavior |
op_drift_predict() |
Predict future drift |
op_drift_overlay() |
Drift‑only overlay |
3. r_Coherence Operators#
| Operator |
Purpose |
op_coherence() |
Compute coherence |
op_coherence_field() |
Identify stable regions |
op_coherence_break() |
Detect coherence loss |
op_coherence_restore() |
Measure recovery |
op_coherence_map() |
Generate coherence map |
op_coherence_profile() |
Summarize coherence behavior |
op_coherence_predict() |
Predict future coherence |
op_coherence_collapse() |
Detect collapse risk |
op_coherence_overlay() |
Coherence‑only overlay |
4. r_Contrast Operators#
| Operator |
Purpose |
op_uptake() |
Measure contrast absorption |
op_washout() |
Measure contrast clearance |
op_enhancement_zone() |
Identify abnormal enhancement |
op_false_uptake() |
Detect artifact‑driven uptake |
op_false_washout() |
Detect artifact‑driven washout |
op_toxicity_corridor() |
Predict contrast toxicity risk |
op_contrast_profile() |
Summarize contrast behavior |
op_contrast_predict() |
Predict contrast behavior |
op_contrast_map() |
Generate contrast map |
op_contrast_overlay() |
Contrast‑only overlay |
5. r_VMRI Operators#
| Operator |
Purpose |
op_vmri_start() |
Initialize VMRI‑Lite simulation |
op_vmri_variant() |
Generate single variant |
op_vmri_batch() |
Generate batch of variants |
op_vmri_corridor() |
Build variant corridor |
op_vmri_pass() |
Extract stable/improving variants |
op_vmri_fail() |
Extract collapse/toxic variants |
op_vmri_optimal() |
Select best predicted outcome |
op_vmri_contrast_predict() |
Predict contrast behavior |
op_vmri_tissue_predict() |
Predict tissue behavior |
op_vmri_profile() |
Summarize VMRI outcomes |
op_vmri_overlay() |
VMRI‑Lite overlay |
6. Canonical Radiology Pipeline#
CAPTURE → FIELD → LAYER → SIGNAL
→ DRIFT → COHERENCE → CONTRAST
→ RESONANCE → VMRI
→ OVERLAY
This is the exact flow your students and AI systems will follow.
7. DOC_MAP#
r_Capture.md
r_Drift.md
r_Coherence.md
r_Contrast.md
r_VMRI.md
r_Overlays.md
r_Index.md
r_Pantheon_Profile.md
r_Glyphs.md
r_Scaffold.md
r_Student_Guide.md
r_Tricorder.md
Index Ready#
Your Radiology Operator Index is now complete and ready for GitHub.
