L1 ⚛ L1-050 ⊙ Testnet

OCT Angiography (OCTA)

Medical Imaging · Motion-contrast OCT for retinal microvasculature

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⬇ principle.md ⬇ principle.json

Fork L1-050

⬇ L1-050.md ⬇ L1-050.json

Prompt — copy into your LLM

You are helping me submit a MODIFICATION of L1-050 (OCT Angiography (OCTA)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-050.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-050.json so EVERY field matches the Markdown.
3. Keep the schema in the "File Mapping" footer at the bottom of the MD.
4. Keep the parent reference unchanged unless I ask otherwise.
Rules: the Markdown is the source of truth; use SI units; do NOT invent benchmark scores.
Output each file in its own fenced code block tagged with the filename.

Here is my template:
[PASTE THE .md HERE]

⚙ Forward Model

y = ∫_t dt `S.scan.repeated` `L.decorrelation` `L.sd_oct_spectral` x + n,    n ~ 𝒩(0, σ²)

world state x (3D vascular network)

→

L · sd oct spectral `L.sd_oct_spectral`

→

L · decorrelation `L.decorrelation`

→

S · scan · repeated `S.scan.repeated`

→

Temporal integration ∫_t dt

→

observation y (photon detector)

Noise: additive Gaussian noise

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-050.md

# ⚛ L1 Principle — OCT Angiography (OCTA) **ID:** `L1-050` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Motion-contrast OCT for retinal microvasculature* > **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** 3D vascular network > **📡 Carrier:** photon · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41553358 --- ## 🧠 1. Introduction **OCT Angiography (OCTA)** is a **nonlinear inverse problem** whose unknown lives in **3D vascular network** space, within the **Motion-contrast OCT for retinal microvasculature** sub-domain of **Medical Imaging**. Measurements consist of photons collected by an optical detector via a **oct angiography** sensing mechanism. The forward operator applies, in order: L · sd oct spectral operator; L · decorrelation operator; S · scan · repeated operator; detector accumulates flux over the exposure window. Observations are corrupted by additive Gaussian noise. Existence of the recovered 3D vascular network is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 13); eye_motion dominates the stability cliff; signal_strength_variation and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point. ## ⚙ 2. Forward Model Physical chain: **x** → L · sd oct spectral → L · decorrelation → S · scan · repeated → Temporal integration → **y** (detector). ``` y = ∫_t dt `S.scan.repeated` `L.decorrelation` `L.sd_oct_spectral` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.sd_oct_spectral` | L · sd oct spectral operator | | `L.decorrelation` | L · decorrelation operator | | `S.scan.repeated` | S · scan · repeated operator | | `int.temporal` | Detector accumulates flux over the exposure window | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Motion-contrast OCT for retinal microvasculature | | Carrier | photon | | Problem class | nonlinear_inverse | | Solution space | 3D_vascular_network | | Noise model | gaussian | | Integration axis | temporal | | Difficulty delta | 3 | | L dag | 3.5 | ## 📡 4. Measurement Model Existence of the recovered 3D vascular network is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 13); eye_motion dominates the stability cliff; signal_strength_variation and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point. | Metric | Value | |---|---| | Metric | PSNR_dB | | Secondary | SSIM | ## 📏 5. Operating Range (Ω) **Center problem class:** `octa` · **Forward operator:** `octa_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 512 | | W | px | 512 | | Z | — | 1024 | | Snr db | dB | 25 | | N repeat | — | 4 | | Eye motion | — | 0 | | Scan pattern | — | raster | | Projection artifact | — | 0 | | Signal strength variation | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 128 | | W | px | 128 | | Z | — | 256 – 2048 | | Snr db | dB | 10.0 – 40.0 | | N repeat | — | 2 – 16 | | Eye motion | — | 0.0 – 0.5 | | Decorrelation tail | — | 0.0 – 0.3 | | Projection artifact | — | 0.0 – 0.3 | | Signal strength variation | — | 0.0 – 0.3 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 25.0 | Metric | Range | |---|---| | Psnr db | 5.0 – 45.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `260` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x111b3a764cdd283c52a2c25ac89883aa73a53e7b76653a02b5d2da2bb31d75d5` - **Chain tx hash:** `0xf956598c8885052f5f0620d5d48acdfad6564a463401b3560511601c34eea2dc` - **Chain block:** `41553358` --- ## File Mapping This bundle consists of: `L1-050.md`, `L1-050.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-050.md` | Source of truth — edit this | Human or LLM | | `L1-050.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-050`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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L2-050-001 OCT Angiography (OCTA) Mismatch-Only Spec

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📋 JSON metadata

{
  "artifact_id": "L1-050",
  "chain_block": 41553358,
  "chain_hash": "0x111b3a764cdd283c52a2c25ac89883aa73a53e7b76653a02b5d2da2bb31d75d5",
  "chain_tx_hash": "0xf956598c8885052f5f0620d5d48acdfad6564a463401b3560511601c34eea2dc",
  "domain": "Medical Imaging",
  "hardness_fn": {
    "delta": 3,
    "kappa": 260,
    "metric": "PSNR_dB",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "PSNR_dB",
    "regime": "Existence of the recovered 3D vascular network is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 13); eye_motion dominates the stability cliff; signal_strength_variation and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.",
    "secondary": "SSIM"
  },
  "physics_fingerprint": {
    "L_DAG": 3.5,
    "carrier": "photon",
    "difficulty_delta": 3,
    "domain": "Medical Imaging",
    "integration_axis": "temporal",
    "noise_model": "gaussian",
    "primitives": [
      "L.sd_oct_spectral",
      "L.decorrelation",
      "S.scan.repeated",
      "int.temporal"
    ],
    "problem_class": "nonlinear_inverse",
    "sensing_mechanism": "oct_angiography",
    "solution_space": "3D_vascular_network",
    "sub_domain": "Motion-contrast OCT for retinal microvasculature",
    "title": "OCT Angiography (OCTA)"
  },
  "size_tiers": {
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      "projection_artifact",
      "eye_motion",
      "signal_strength_variation",
      "projection_artifact",
      "decorrelation_tail"
    ],
    "allowed_problem_classes": [
      "octa"
    ],
    "center_spec": {
      "epsilon_fn_center": "25.0",
      "forward_operator": "octa_forward",
      "input_format": "measurement_only",
      "omega": {
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        "N_repeat": 4,
        "SNR_dB": 25,
        "W": 512,
        "Z": 1024,
        "eye_motion": 0.0,
        "projection_artifact": 0.0,
        "scan_pattern": "raster",
        "signal_strength_variation": 0.0
      },
      "problem_class": "octa"
    },
    "epsilon_bounds": {
      "psnr_db": [
        5.0,
        45.0
      ]
    },
    "omega_bounds": {
      "H": 128,
      "N_repeat": [
        2,
        16
      ],
      "SNR_dB": [
        10.0,
        40.0
      ],
      "W": 128,
      "Z": [
        256,
        2048
      ],
      "decorrelation_tail": [
        0.0,
        0.3
      ],
      "eye_motion": [
        0.0,
        0.5
      ],
      "projection_artifact": [
        0.0,
        0.3
      ],
      "signal_strength_variation": [
        0.0,
        0.3
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Motion-contrast OCT for retinal microvasculature",
  "title": "OCT Angiography (OCTA)"
}

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