L1 ⚛ L1-514 ⊙ Testnet

Chest CT Pneumonia/COVID Severity Classification (PWDR)

Medical Imaging · Lung tissue density reconstruction with pneumonia/COVID severity-grade categorical readout

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⬇ L1-514.md

# ⚛ L1 Principle — Chest CT Pneumonia/COVID Severity Classification (PWDR) **ID:** `L1-514` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Lung tissue density reconstruction with pneumonia/COVID severity-grade categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D severity grade > **📡 Carrier:** x_ray · **🌫 Noise:** poisson > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41552305 --- ## 🧠 1. Introduction **Chest CT Pneumonia/COVID Severity Classification (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D severity grade** space, within the **Lung tissue density reconstruction with pneumonia/COVID severity-grade categorical readout** sub-domain of **Medical Imaging**. Measurements consist of X-ray photons transmitted through (or scattered by) the sample via a **ct attenuation with tss corads grading** sensing mechanism. The forward operator applies, in order: polyenergetic X-ray emission spectrum; line-integral projection through an attenuation map; L · filtered back projection operator; L · lobe segmentation operator; L · density thresholding operator; L · tss classifier operator; pixel-level spatial averaging on the detector. Observations are corrupted by Poisson counting noise. Existence and uniqueness inherited from L1-029 CT. Stability inherits L1-029's well-conditioned reconstruction plus a small additive contribution from threshold_calibration_uncertainty. Joint Hadamard well-posedness for the coupled CT-reconstruction + TSS/CO-RADS forward established by Pan F et al. (2020, foundational TSS), Francone M et al. (2020 TSS validation), Prokop M et al. (2020 CO-RADS Dutch consensus), Inui S et al. (2020 multicenter TSS), Liu Z et al. (2021 deep-learning TSS automation), and Lessmann N et al. (2021 multicentre CO-RADS validation). ## ⚙ 2. Forward Model Physical chain: **x** → X-ray source → Attenuation projection → L · lobe segmentation → L · density thresholding → L · tss classifier → Spatial integration → **y** (detector). ``` y = ∫_A dA `L.tss_classifier` `L.density_thresholding` `L.lobe_segmentation` ∫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.lobe_segmentation` | L · lobe segmentation operator | | `L.density_thresholding` | L · density thresholding operator | | `L.tss_classifier` | L · tss classifier operator | | `int.spatial` | Pixel-level spatial averaging on the detector | **🛰 Estimator components** _(used inside the solver, not in the forward equation)_: | Primitive | What it does | |---|---| | `L.filtered_back_projection` | L · filtered back projection operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Lung tissue density reconstruction with pneumonia/COVID severity-grade categorical readout | | Carrier | x_ray | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_severity_grade | | Noise model | poisson | | Integration axis | spatial | | Difficulty delta | 3 | | L dag | 5.7 | ## 📡 4. Measurement Model Existence and uniqueness inherited from L1-029 CT. Stability inherits L1-029's well-conditioned reconstruction plus a small additive contribution from threshold_calibration_uncertainty. Joint Hadamard well-posedness for the coupled CT-reconstruction + TSS/CO-RADS forward established by Pan F et al. (2020, foundational TSS), Francone M et al. (2020 TSS validation), Prokop M et al. (2020 CO-RADS Dutch consensus), Inui S et al. (2020 multicenter TSS), Liu Z et al. (2021 deep-learning TSS automation), and Lessmann N et al. (2021 multicentre CO-RADS validation). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | weighted_kappa_severity | ## 📏 5. Operating Range (Ω) **Center problem class:** `chest_ct_tss_pwdr` · **Forward operator:** `ct_severity_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 512 | | W | px | 512 | | Z | — | 200 | | Kvp | — | 120 | | Mas | — | 200 | | Snr db | dB | 30 | | Voxel size mm | mm | 0.7 | | Slice thickness mm | mm | 1.5 | | Kv calibration drift | — | 0 | | Lobe segmentation error | — | 0 | | Motion blur breath hold | — | 0 | | Contrast enhancement state | — | 0 | | Slice thickness anisotropy | — | 0 | | Threshold calibration uncertainty | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 256 – 1024 | | W | px | 256 – 1024 | | Z | — | 50 – 1000 | | Kvp | — | 80 – 140 | | Slice thickness mm | mm | 0.5 – 5.0 | ## 🎯 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 = `50` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x2fb59dc5d9769f93b04ba3a446df698ef8ccd260c27f1e246b1fb3563104b761` - **Chain tx hash:** `0x7b91e1110d03eedaa298d94c56f99fc726a9080e5db9c35829d538c9f7f25e7e` - **Chain block:** `41552305` --- ## File Mapping This bundle consists of: `L1-514.md`, `L1-514.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-514.md` | Source of truth — edit this | Human or LLM | | `L1-514.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 `L1-514`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-514.md).
Regenerate L1-514.json so every field matches the Markdown.
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L2-514-001 Chest CT Pneumonia/COVID Severity Classification (PWDR) — Spec Skeleton

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📋 JSON metadata

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  "chain_block": 41552305,
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  "chain_tx_hash": "0x7b91e1110d03eedaa298d94c56f99fc726a9080e5db9c35829d538c9f7f25e7e",
  "domain": "Medical Imaging",
  "hardness_fn": {
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    "kappa": 50,
    "metric": "categorical_accuracy",
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      "name": "primary",
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  "observable_profile": {
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    "regime": "Existence and uniqueness inherited from L1-029 CT. Stability inherits L1-029\u0027s well-conditioned reconstruction plus a small additive contribution from threshold_calibration_uncertainty. Joint Hadamard well-posedness for the coupled CT-reconstruction + TSS/CO-RADS forward established by Pan F et al. (2020, foundational TSS), Francone M et al. (2020 TSS validation), Prokop M et al. (2020 CO-RADS Dutch consensus), Inui S et al. (2020 multicenter TSS), Liu Z et al. (2021 deep-learning TSS automation), and Lessmann N et al. (2021 multicentre CO-RADS validation).",
    "secondary": "weighted_kappa_severity"
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  "physics_fingerprint": {
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    "carrier": "x_ray",
    "difficulty_delta": 3,
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    "integration_axis": "spatial",
    "noise_model": "poisson",
    "primitives": [
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      "L.filtered_back_projection",
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      "L.tss_classifier",
      "int.spatial"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "ct_attenuation_with_tss_corads_grading",
    "solution_space": "1D_severity_grade",
    "sub_domain": "Lung tissue density reconstruction with pneumonia/COVID severity-grade categorical readout",
    "title": "Chest CT Pneumonia/COVID Severity Classification (PWDR)"
  },
  "size_tiers": {
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    "allowed_omega_dimensions": [
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    ],
    "center_spec": {
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      "input_format": "chest_ct_volume_with_severity_grade",
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        "voxel_size_mm": 0.7
      },
      "problem_class": "chest_ct_tss_pwdr"
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    "epsilon_bounds": {
      "categorical_accuracy": [
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      ]
    },
    "omega_bounds": {
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        1024
      ],
      "W": [
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      "Z": [
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      ],
      "kVp": [
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        140
      ],
      "slice_thickness_mm": [
        0.5,
        5.0
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Lung tissue density reconstruction with pneumonia/COVID severity-grade categorical readout",
  "title": "Chest CT Pneumonia/COVID Severity Classification (PWDR)"
}

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