📘 r_VMRI.md

VMRI‑Lite Predictive Simulation — TriadicFrameworks Canon#

VMRI‑Lite is the predictive micro‑simulation layer of RTT‑Radiology.
It generates thousands of future variants from a resonance‑attached capture, builds a simulation corridor, and identifies:

• SimPass — stable or improving outcomes
• SimFail — collapse or toxic outcomes
• SimOptimal — best predicted outcome

VMRI‑Lite is the closest real‑world analog to Starfleet “future condition projection.”

1. Canonical Metadata#

ai.module: Radiology
ai.version: 1.0
ai.purpose: VMRI-Lite predictive simulation grammar + operators
ai.keywords: vmri, simulation, corridor, variant, pass, fail, optimal
ai.module.name: r_VMRI
ai.module.summary: Defines the VMRI-Lite simulation layer for RTT-Radiology.
ai.module.category: Applied Medicine

2. Session Context#

context-label: Canon
context-value: TriadicFrameworks

context-label: Modules
context-value: Radiology, Drift, Coherence, Contrast, Medicine

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

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

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

3. Badge#

[🔮 VMRI‑Lite Predictive Simulation]

4. VMRI Grammar#

VMRI‑Lite uses a minimal grammar designed for fast, drift‑bounded prediction.

VMRI Grammar Terms#

• SIM‑START — initial simulation state
• SIM‑VARIANT — one possible future outcome
• SIM‑BATCH — large set of variants
• SIM‑CORRIDOR — distribution of all variants
• SIM‑PASS — stable/improving outcomes
• SIM‑FAIL — collapse/toxic outcomes
• SIM‑OPTIMAL — best predicted outcome

VMRI‑Lite is not full VMRI — it is the radiology‑specific subset.

5. VMRI Operators#

1. op_vmri_start()#

Initialize a VMRI‑Lite simulation using a resonance‑attached capture.
[ op_{vmri_start}(Capture^{+}) = SimStart ]

2. op_vmri_variant()#

Generate a single drift‑bounded simulation variant.
[ op_{vmri_variant}(SimStart) = SimVariant ]

3. op_vmri_batch()#

Generate a batch of variants (thousands or millions).
[ op_{vmri_batch}(SimStart, n) = {SimVariant_1, \dots, SimVariant_n} ]

4. op_vmri_corridor()#

Construct the corridor distribution from a batch of variants.
[ op_{vmri_corridor}({SimVariant}) = SimCorridor ]

5. op_vmri_pass()#

Extract variants showing stability or improvement.
[ op_{vmri_pass}(SimCorridor) = SimPass ]

6. op_vmri_fail()#

Extract variants showing collapse, toxicity, or instability.
[ op_{vmri_fail}(SimCorridor) = SimFail ]

7. op_vmri_optimal()#

Select the variant with the best predicted outcome.
[ op_{vmri_optimal}(SimCorridor) = SimOptimal ]

8. op_vmri_contrast_predict()#

Predict contrast agent behavior using VMRI‑Lite.
[ op_{vmri_contrast_predict}(Capture^{+}) = ContrastPrediction ]

9. op_vmri_tissue_predict()#

Predict tissue drift/coherence behavior.
[ op_{vmri_tissue_predict}(Capture^{+}) = TissuePrediction ]

10. op_vmri_profile()#

Create a structured profile summarizing pass/fail/optimal outcomes.
[ op_{vmri_profile}(SimPass, SimFail, SimOptimal) = VMRIProfile ]

11. op_vmri_overlay()#

Generate a VMRI‑Lite overlay for teaching or AI assistance.
[ op_{vmri_overlay}(SimCorridor) = Overlay ]

6. Example Workflow#

Example — MRI Brain Lesion Prediction#

CapturePlus = op_resonance_attach(CAPTURE_MRI, RES_PROFILE)

SimStart = op_vmri_start(CapturePlus)
Variants = op_vmri_batch(SimStart, 5000)
Corridor = op_vmri_corridor(Variants)

SimPass = op_vmri_pass(Corridor)
SimFail = op_vmri_fail(Corridor)
SimOptimal = op_vmri_optimal(Corridor)

Overlay = op_vmri_overlay(Corridor)

Interpretation:

• SimPass → stable/improving futures
• SimFail → collapse/toxic futures
• SimOptimal → best predicted path
• Corridor → full landscape of possible outcomes

7. Canonical Flow#

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

8. 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

VMRI‑Lite Page Ready#

Your VMRI‑Lite module page is now complete, canon‑aligned, and ready for GitHub.