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šŸ“˜ Radiology Student Guide

How to Perform RTTā€‘Radiology Analysis ā€” TriadicFrameworks Canon#

This guide teaches students, radiologists, and AI systems how to perform a full RTTā€‘Radiology analysis using the Radiology grammar:

  • Capture
  • Drift
  • Coherence
  • Contrast
  • Resonance
  • VMRIā€‘Lite
  • Overlay

It is the practical workflow for the Radiology module.

1. Canonical Metadata#

ai.module: Radiology
ai.version: 1.0
ai.purpose: Student guide for RTTā€‘Radiology analysis
ai.keywords: student guide, workflow, drift, coherence, contrast, vmri-lite
ai.module.name: r_Student_Guide
ai.module.summary: Step-by-step instructions for performing RTTā€‘Radiology analysis.
ai.module.category: Applied Medicine

2. Session Context#

context-label: Canon
context-value: TriadicFrameworks

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

context-label: Format
context-value: Student workflow + examples

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

context-label: Audience
context-value: Radiology students, medical AI systems, imaging researchers

3. Badge#

[šŸŽ“ RTTā€‘Radiology Student Guide]

4. Overview#

RTTā€‘Radiology teaches students to ā€œsee moreā€ inside medical imaging by analyzing:

  • change (Drift)
  • stability (Coherence)
  • chemical behavior (Contrast)
  • future outcomes (VMRIā€‘Lite)

This guide provides the canonical workflow for performing a full RTTā€‘Radiology analysis.

5. The RTTā€‘Radiology Workflow#

The workflow always follows the same pipeline:

CAPTURE ā†’ FIELD ā†’ LAYER ā†’ SIGNAL
ā†’ DRIFT ā†’ COHERENCE ā†’ CONTRAST
ā†’ RESONANCE ā†’ VMRI
ā†’ OVERLAY

Each step is explained below.

6. Stepā€‘byā€‘Step Instructions#

Step 1 ā€” CAPTURE#

Start with any imaging modality:

  • CT
  • MRI
  • Xā€‘ray
  • Ultrasound
  • PET

Use:

Field = op_field(CAPTURE, "region")
Layer = op_layer(Field, layerType)
Signal = op_signal(Layer)

Goal: Extract the region and layer you want to analyze.

Step 2 ā€” DRIFT (What is changing?)#

Drift shows progression, migration, and instability.

Drift = op_drift(Signal_T1, Signal_T2)
Velocity = op_drift_velocity(Drift, Ī”t)
Vector = op_drift_vector(Field_T1, Field_T2)
DriftMap = op_drift_map(Field)

Student interpretation:

  • High drift ā†’ active change
  • High velocity ā†’ rapid progression
  • Drift vector ā†’ direction of change
  • Drift map ā†’ spatial visualization

Step 3 ā€” COHERENCE (What is stable?)#

Coherence shows stability, healing, and collapse risk.

Coherence = op_coherence(Field)
BreakZone = op_coherence_break(Coherence)
Restore = op_coherence_restore(Coh_T1, Coh_T2)
CohMap = op_coherence_map(Field)

Student interpretation:

  • BreakZone ā†’ early pathology
  • Restore ā†’ healing trajectory
  • Collapse risk ā†’ structural failure prediction

Step 4 ā€” CONTRAST (What is reacting?)#

Contrast shows chemical behavior inside tissues.

Uptake = op_uptake(ContrastLayer)
Washout = op_washout(ContrastLayer_T1, ContrastLayer_T2)
EnhancementZone = op_enhancement_zone(Uptake, Washout)
FalseUptake = op_false_uptake(Uptake, Noise)
FalseWashout = op_false_washout(Washout, Noise)
ContrastMap = op_contrast_map(ContrastLayer)

Student interpretation:

  • Uptake ā†’ absorption
  • Washout ā†’ clearance
  • Enhancement ā†’ abnormal chemical activity
  • False signals ā†’ artifact suppression

Step 5 ā€” RESONANCE (Attach patient profile)#

Resonance attaches patientā€‘specific behavior to the capture.

CapturePlus = op_resonance_attach(CAPTURE, RES_PROFILE)
ResPredict = op_resonance_predict(CapturePlus)

Student interpretation:

  • Resonance modifies drift/coherence/contrast predictions
  • It personalizes the analysis

Step 6 ā€” VMRIā€‘Lite (Predict the future)#

VMRIā€‘Lite simulates future outcomes.

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)

Student interpretation:

  • Pass ā†’ stable/improving outcomes
  • Fail ā†’ collapse/toxic outcomes
  • Optimal ā†’ best predicted outcome

Step 7 ā€” OVERLAY (Combine everything)#

Create a unified RTTā€‘Radiology overlay.

Overlay = op_overlay(CAPTURE, DriftMap, CohMap, EnhancementZone)

Overlays help students visualize:

  • drift
  • coherence
  • contrast
  • VMRI corridors

all at once.

7. Example Full Workflow#

MRI Brain Lesion#

Field = op_field(CAPTURE_MRI, "left-parietal")
Layer = op_layer(Field, contrast)

Uptake = op_uptake(Layer)
Washout = op_washout(Layer_T1, Layer_T2)
EnhancementZone = op_enhancement_zone(Uptake, Washout)

Coherence = op_coherence(Field)
CohMap = op_coherence_map(Field)

Drift = op_drift(Signal_T1, Signal_T2)
DriftMap = op_drift_map(Field)

CapturePlus = op_resonance_attach(CAPTURE_MRI, RES_PROFILE)
Corridor = op_vmri_corridor(op_vmri_batch(op_vmri_start(CapturePlus), 5000))

Overlay = op_overlay(CAPTURE_MRI, DriftMap, CohMap, EnhancementZone)

8. Student Tips#

  • Drift shows what is changing
  • Coherence shows what is stable
  • Contrast shows what is reacting
  • VMRI shows what will happen next
  • Overlays show everything at once

9. 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

Student Guide Ready#

Your Radiology Student Guide is now complete, canonā€‘aligned, and ready for GitHub.

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