L1 ⚛ L1-039 ⊙ Testnet

X-ray Angiography (DSA)

Medical Imaging · Subtraction-based vascular imaging

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⬇ principle.md ⬇ principle.json

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⬇ L1-039.md ⬇ L1-039.json

Prompt — copy into your LLM

You are helping me submit a MODIFICATION of L1-039 (X-ray Angiography (DSA)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-039.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-039.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 `L.digital_subtraction` `L.contrast_bolus` I₀(E) x,    measurements ~ Poisson(αy)

world state x (2D vessel map)

→

X-ray source I₀(E)

→

L · contrast bolus `L.contrast_bolus`

→

L · digital subtraction `L.digital_subtraction`

→

Temporal integration ∫_t dt

→

observation y (x ray detector)

Noise: Poisson shot noise from quantum-limited detection

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-039.md

# ⚛ L1 Principle — X-ray Angiography (DSA) **ID:** `L1-039` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Subtraction-based vascular imaging* > **🎯 Problem class:** linear inverse · **🧮 Solution space:** 2D vessel map > **📡 Carrier:** x_ray · **🌫 Noise:** shot poisson > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41553371 --- ## 🧠 1. Introduction **X-ray Angiography (DSA)** is a **linear inverse problem** whose unknown lives in **2D vessel map** space, within the **Subtraction-based vascular imaging** sub-domain of **Medical Imaging**. Measurements consist of X-ray photons transmitted through (or scattered by) the sample via a **xray projection** sensing mechanism. The forward operator applies, in order: polyenergetic X-ray emission spectrum; L · contrast bolus operator; L · digital subtraction operator; detector accumulates flux over the exposure window. Observations are corrupted by Poisson shot noise from quantum-limited detection. Existence of the recovered 2D vessel map is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by the declared priors. Stability is moderately conditioned (kappa_eff ~= 10); contrast_timing dominates the stability cliff; patient_motion and the remaining mismatch parameters contribute higher-order bias terms. Photon-shot-noise-limited (poisson counting) 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** → X-ray source → L · contrast bolus → L · digital subtraction → Temporal integration → **y** (detector). ``` y = ∫_t dt `L.digital_subtraction` `L.contrast_bolus` I₀(E) x, measurements ~ Poisson(αy) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.xray_source` | Polyenergetic x-ray emission spectrum | | `L.contrast_bolus` | L · contrast bolus operator | | `L.digital_subtraction` | L · digital subtraction operator | | `int.temporal` | Detector accumulates flux over the exposure window | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Subtraction-based vascular imaging | | Carrier | x_ray | | Problem class | linear_inverse | | Solution space | 2D_vessel_map | | Noise model | shot_poisson | | Integration axis | temporal | | Difficulty delta | 3 | | L dag | 3 | ## 📡 4. Measurement Model Existence of the recovered 2D vessel map is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by the declared priors. Stability is moderately conditioned (kappa_eff ~= 10); contrast_timing dominates the stability cliff; patient_motion and the remaining mismatch parameters contribute higher-order bias terms. Photon-shot-noise-limited (poisson counting) 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:** `xray_angio` · **Forward operator:** `xray_angio_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 1024 | | W | px | 1024 | | Fps | — | 15 | | Kvp | — | 80 | | Pixel um | µm | 150 | | Patient motion | — | 0 | | Contrast timing | — | 0 | | Misregistration | — | 0 | | Dose per frame ugy | — | 5 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 512 | | W | px | 512 | | Fps | — | 1 – 60 | | Kvp | — | 60 – 120 | | Scatter | — | 0.0 – 0.3 | | Pixel um | µm | 50 – 300 | | Patient motion | — | 0.0 – 0.3 | | Contrast timing | — | 0.0 – 0.5 | | Misregistration | — | 0.0 – 5.0 | | Dose per frame ugy | — | 0.5 – 50 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 28.0 | Metric | Range | |---|---| | Psnr db | 5.0 – 45.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `200` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x08c99ed291c11a03c8c90d01c7e235e6b6fb39c334818028fa527bfef37a1efa` - **Chain tx hash:** `0xf091da089367fb0c8633564faf2d28ff0f11f09ffa956bba6e011430b671f1d4` - **Chain block:** `41553371` --- ## File Mapping This bundle consists of: `L1-039.md`, `L1-039.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-039.md` | Source of truth — edit this | Human or LLM | | `L1-039.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 `L1-039`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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L2-039-001 X-ray Angiography (DSA) Mismatch-Only Spec

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📋 JSON metadata

{
  "artifact_id": "L1-039",
  "chain_block": 41553371,
  "chain_hash": "0x08c99ed291c11a03c8c90d01c7e235e6b6fb39c334818028fa527bfef37a1efa",
  "chain_tx_hash": "0xf091da089367fb0c8633564faf2d28ff0f11f09ffa956bba6e011430b671f1d4",
  "domain": "Medical Imaging",
  "hardness_fn": {
    "delta": 3,
    "kappa": 200,
    "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 2D vessel map is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by the declared priors. Stability is moderately conditioned (kappa_eff ~= 10); contrast_timing dominates the stability cliff; patient_motion and the remaining mismatch parameters contribute higher-order bias terms. Photon-shot-noise-limited (poisson counting) 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": 3.0,
    "carrier": "x_ray",
    "difficulty_delta": 3,
    "domain": "Medical Imaging",
    "integration_axis": "temporal",
    "noise_model": "shot_poisson",
    "primitives": [
      "L.xray_source",
      "L.contrast_bolus",
      "L.digital_subtraction",
      "int.temporal"
    ],
    "problem_class": "linear_inverse",
    "sensing_mechanism": "xray_projection",
    "solution_space": "2D_vessel_map",
    "sub_domain": "Subtraction-based vascular imaging",
    "title": "X-ray Angiography (DSA)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "xray_angio_forward"
    ],
    "allowed_omega_dimensions": [
      "H",
      "W",
      "fps",
      "kVp",
      "dose_per_frame_uGy",
      "pixel_um",
      "contrast_timing",
      "patient_motion",
      "misregistration",
      "contrast_timing",
      "patient_motion",
      "misregistration",
      "scatter"
    ],
    "allowed_problem_classes": [
      "xray_angio"
    ],
    "center_spec": {
      "epsilon_fn_center": "28.0",
      "forward_operator": "xray_angio_forward",
      "input_format": "measurement_only",
      "omega": {
        "H": 1024,
        "W": 1024,
        "contrast_timing": 0.0,
        "dose_per_frame_uGy": 5,
        "fps": 15,
        "kVp": 80,
        "misregistration": 0.0,
        "patient_motion": 0.0,
        "pixel_um": 150
      },
      "problem_class": "xray_angio"
    },
    "epsilon_bounds": {
      "psnr_db": [
        5.0,
        45.0
      ]
    },
    "omega_bounds": {
      "H": 512,
      "W": 512,
      "contrast_timing": [
        0.0,
        0.5
      ],
      "dose_per_frame_uGy": [
        0.5,
        50
      ],
      "fps": [
        1,
        60
      ],
      "kVp": [
        60,
        120
      ],
      "misregistration": [
        0.0,
        5.0
      ],
      "patient_motion": [
        0.0,
        0.3
      ],
      "pixel_um": [
        50,
        300
      ],
      "scatter": [
        0.0,
        0.3
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Subtraction-based vascular imaging",
  "title": "X-ray Angiography (DSA)"
}

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