L1 ⚛ L1-524 ⊙ Testnet

Stroke Ischemic Core / Penumbra Classification from CT-Perfusion (PWDR)

Medical Imaging · Cerebral hemodynamics recovery from dynamic CT-perfusion with mismatch-based stroke triage readout

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⬇ L1-524.md

# ⚛ L1 Principle — Stroke Ischemic Core / Penumbra Classification from CT-Perfusion (PWDR) **ID:** `L1-524` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Cerebral hemodynamics recovery from dynamic CT-perfusion with mismatch-based stroke triage readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D stroke triage label > **📡 Carrier:** x_ray · **🌫 Noise:** poisson > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41553360 --- ## 🧠 1. Introduction **Stroke Ischemic Core / Penumbra Classification from CT-Perfusion (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D stroke triage label** space, within the **Cerebral hemodynamics recovery from dynamic CT-perfusion with mismatch-based stroke triage readout** sub-domain of **Medical Imaging**. Measurements consist of X-ray photons transmitted through (or scattered by) the sample via a **ct perfusion with stroke triage threshold** sensing mechanism. The forward operator applies, in order: polyenergetic X-ray emission spectrum; line-integral projection through an attenuation map; L · dynamic reconstruction operator; L · deconvolution aif operator; L · cbf cbv mtt tmax operator; L · ctp threshold classifier operator; int · spatial temporal operator. Observations are corrupted by Poisson counting noise. Existence inherited from L1-029. Uniqueness conditional on AIF selection (manual or automated) and temporal sampling adequacy. Stability conditional with deconvolution_regularization dominant for noise sensitivity; threshold_calibration_vendor_difference contributes inter-vendor bias (~20% volume estimation difference between RAPID and Olea). Joint Hadamard well-posedness for the coupled dynamic-CTP + perfusion-threshold forward established by Ostergaard 1996 (foundational SVD deconvolution), Konstas 2009 (CTP technical review), Wintermark 2006 (perfusion thresholds), Albers 2018 (DEFUSE-3 trial), Nogueira 2018 (DAWN trial), Goyal 2016 (HERMES meta-analysis). ## ⚙ 2. Forward Model Physical chain: **x** → X-ray source → Attenuation projection → L · dynamic reconstruction → L · deconvolution aif → L · cbf cbv mtt tmax → L · ctp threshold classifier → int · spatial temporal → **y** (detector). ``` y = `int.spatial_temporal` `L.ctp_threshold_classifier` `L.cbf_cbv_mtt_tmax` `L.deconvolution_aif` `L.dynamic_reconstruction` ∫µ dl I₀(E) x, measurements ~ Poisson(αy) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.xray_source` | Polyenergetic x-ray emission spectrum | | `L.attenuation_projection` | Line-integral projection through an attenuation map | | `L.dynamic_reconstruction` | L · dynamic reconstruction operator | | `L.deconvolution_aif` | L · deconvolution aif operator | | `L.cbf_cbv_mtt_tmax` | L · cbf cbv mtt tmax operator | | `L.ctp_threshold_classifier` | L · ctp threshold classifier operator | | `int.spatial_temporal` | Int · spatial temporal operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Cerebral hemodynamics recovery from dynamic CT-perfusion with mismatch-based stroke triage readout | | Carrier | x_ray | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_stroke_triage_label | | Noise model | poisson | | Integration axis | spatial_temporal | | Difficulty delta | 5 | | L dag | 7.3 | ## 📡 4. Measurement Model Existence inherited from L1-029. Uniqueness conditional on AIF selection (manual or automated) and temporal sampling adequacy. Stability conditional with deconvolution_regularization dominant for noise sensitivity; threshold_calibration_vendor_difference contributes inter-vendor bias (~20% volume estimation difference between RAPID and Olea). Joint Hadamard well-posedness for the coupled dynamic-CTP + perfusion-threshold forward established by Ostergaard 1996 (foundational SVD deconvolution), Konstas 2009 (CTP technical review), Wintermark 2006 (perfusion thresholds), Albers 2018 (DEFUSE-3 trial), Nogueira 2018 (DAWN trial), Goyal 2016 (HERMES meta-analysis). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | RMSE_core_volume_mL | ## 📏 5. Operating Range (Ω) **Center problem class:** `stroke_ctp_triage_pwdr` · **Forward operator:** `ctp_stroke_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 512 | | W | px | 512 | | Z | — | 32 | | Kvp | — | 80 | | Mas | — | 100 | | N time frames | — | 60 | | Voxel size mm | mm | 0.7 | | Frame period s | s | 1.5 | | Contrast dose ml | — | 50 | | Contrast bolus timing | — | 0 | | Aif selection uncertainty | — | 0 | | Motion during acquisition | — | 0 | | Partial volume at arteries | — | 0 | | Deconvolution regularization | — | 0 | | Threshold calibration vendor difference | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 256 – 1024 | | W | px | 256 – 1024 | | Z | — | 8 – 80 | | N time frames | — | 20 – 120 | | Frame period s | s | 1.0 – 3.0 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.85_accuracy | Metric | Range | |---|---| | Categorical accuracy | 0.5 – 0.99 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **categorical_accuracy**, with κ = `200` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xaa563431da0e16ed0eff5ab2b07a375ed2b1d32be1b208ecabb50bb303358a48` - **Chain tx hash:** `0xb7c16c02abb69d9279620d8ee881383fa07845e9dc2605ba8164b0db37f8e19d` - **Chain block:** `41553360` --- ## File Mapping This bundle consists of: `L1-524.md`, `L1-524.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-524.md` | Source of truth — edit this | Human or LLM | | `L1-524.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 `L1-524`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-524.md).
Regenerate L1-524.json so every field matches the Markdown.
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L2-524-001 Stroke Ischemic Core / Penumbra Classification from CT-Perfusion (PWDR) — Spec Skeleton

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📋 JSON metadata

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  "chain_block": 41553360,
  "chain_hash": "0xaa563431da0e16ed0eff5ab2b07a375ed2b1d32be1b208ecabb50bb303358a48",
  "chain_tx_hash": "0xb7c16c02abb69d9279620d8ee881383fa07845e9dc2605ba8164b0db37f8e19d",
  "domain": "Medical Imaging",
  "hardness_fn": {
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    "kappa": 200,
    "metric": "categorical_accuracy",
    "type": "epsilon_fn"
  },
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      "name": "primary",
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    }
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  "layer": "L1",
  "observable_profile": {
    "metric": "categorical_accuracy",
    "regime": "Existence inherited from L1-029. Uniqueness conditional on AIF selection (manual or automated) and temporal sampling adequacy. Stability conditional with deconvolution_regularization dominant for noise sensitivity; threshold_calibration_vendor_difference contributes inter-vendor bias (~20% volume estimation difference between RAPID and Olea). Joint Hadamard well-posedness for the coupled dynamic-CTP + perfusion-threshold forward established by Ostergaard 1996 (foundational SVD deconvolution), Konstas 2009 (CTP technical review), Wintermark 2006 (perfusion thresholds), Albers 2018 (DEFUSE-3 trial), Nogueira 2018 (DAWN trial), Goyal 2016 (HERMES meta-analysis).",
    "secondary": "RMSE_core_volume_mL"
  },
  "physics_fingerprint": {
    "L_DAG": 7.3,
    "carrier": "x_ray",
    "difficulty_delta": 5,
    "domain": "Medical Imaging",
    "integration_axis": "spatial_temporal",
    "noise_model": "poisson",
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      "L.dynamic_reconstruction",
      "L.deconvolution_aif",
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      "L.ctp_threshold_classifier",
      "int.spatial_temporal"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "ct_perfusion_with_stroke_triage_threshold",
    "solution_space": "1D_stroke_triage_label",
    "sub_domain": "Cerebral hemodynamics recovery from dynamic CT-perfusion with mismatch-based stroke triage readout",
    "title": "Stroke Ischemic Core / Penumbra Classification from CT-Perfusion (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "ctp_stroke_pwdr_forward",
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      "ctp_dawn_defuse3_pwdr_forward"
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      "contrast_dose_mL",
      "AIF_selection_uncertainty",
      "motion_during_acquisition",
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      "contrast_bolus_timing",
      "deconvolution_regularization",
      "threshold_calibration_vendor_difference"
    ],
    "allowed_problem_classes": [
      "stroke_ctp_triage_pwdr",
      "stroke_ischemic_core_pwdr",
      "stroke_penumbra_pwdr",
      "stroke_thrombectomy_eligibility_pwdr"
    ],
    "center_spec": {
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      "omega": {
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        "H": 512,
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        "frame_period_s": 1.5,
        "kVp": 80,
        "mAs": 100,
        "motion_during_acquisition": 0.0,
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        "threshold_calibration_vendor_difference": 0.0,
        "voxel_size_mm": 0.7
      },
      "problem_class": "stroke_ctp_triage_pwdr"
    },
    "epsilon_bounds": {
      "categorical_accuracy": [
        0.5,
        0.99
      ]
    },
    "omega_bounds": {
      "H": [
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        1024
      ],
      "N_time_frames": [
        20,
        120
      ],
      "W": [
        256,
        1024
      ],
      "Z": [
        8,
        80
      ],
      "frame_period_s": [
        1.0,
        3.0
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Cerebral hemodynamics recovery from dynamic CT-perfusion with mismatch-based stroke triage readout",
  "title": "Stroke Ischemic Core / Penumbra Classification from CT-Perfusion (PWDR)"
}

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