Stroke Ischemic Core / Penumbra Classification from CT-Perfusion (PWDR) L1-524
Unclaimed Principle — open for contribution
This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.
To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.
Forward model E
Stage 1 (L1-029 + temporal deconvolution): {y_proj(t)} \to mu_attenuation(r, t); deconvolution(C(r, t), AIF(t)) \to R(r, t) residue function \to CBF, CBV, MTT, Tmax. Stage 2: ischemic_core_mask = (rCBF<0.30) | (CBV<2); penumbra_mask = (Tmax>6) & ~core_mask; triage = mismatch-volume rules per DAWN/DEFUSE-3.Stroke Ischemic Core / Penumbra Classification (PWDR): wraps L1-029 CT analytical core extended for dynamic perfusion imaging with established CTP threshold rules. Stage 1 (analytical, from L1-029 + dynamic deconvolution): from time-series CTP acquisition (typically 60-90 frames over 50-70 seconds following IV iodinated contrast bolus), recover voxel-resolved hemodynamic parameter maps via SVD-based deconvolution against arterial input function: CBF (cerebral blood flow), CBV (cerebral blood volume), MTT (mean transit time), Tmax (time to maximum residue function). Stage 2 (deterministic threshold): apply RAPID / Olea / Syngo.via thresholds to identify ischemic core (rCBF<0.30 or CBV<2) and penumbra (Tmax>6s); compute volumes; classify into mechanical thrombectomy triage. Difficulty tier delta = 5. Mismatch parameters: AIF_selection_uncertainty, motion_during_acquisition, partial_volume_at_arteries, contrast_bolus_timing, deconvolution_regularization, threshold_calibration_vendor_difference.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- conditional
- Stability:
- conditional
- κ:
- 200
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).
Solvability C
- Solver class:
- linear-operator + convex optimisation [L1-029 reconstruction + SVD/oSVD deconvolution + threshold] | end-to-end deep neural [DeepStroke, RAPID Vision, Brainomix e-Stroke] with explicit physics-informed perfusion-parameter regularization
- Convergence rate q:
- 2
- Complexity:
- O(H * W * Z * N_time_frames * log(N_time_frames)) per iteration; deep neural variants O(H W Z N_time_frames * F_theta_cost)