MR Spectroscopy Tumor Grading (PWDR) L1-516
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
MR Spectroscopy Tumor Grading (PWDR): wraps L1-044 MRS analytical core with established metabolite-ratio threshold rules from neuro-oncology, prostate cancer, and breast cancer literature. Stage 1 (analytical, from L1-044): voxel-resolved 1H-MRS recovers metabolite concentrations (NAA, Cho, Cr, Cit, lactate, lipid, mI) from chemical-shift-resolved acquisition. Stage 2 (deterministic threshold): per-organ rule applies metabolite-ratio thresholds (Cho/NAA, Cho/Cr, (Cho+Cr)/Cit, Cho/water) to assign tumor grade. Difficulty tier delta = 3. Mismatch parameters: voxel_lipid_contamination, B0_inhomogeneity, water_suppression_residual, partial_volume_csf, threshold_calibration_age_effect.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- conditional
- Stability:
- conditional
- κ:
- 120
Existence and uniqueness inherited from L1-044 MRS. Stability inherits L1-044's kappa_eff plus additive contribution from voxel_lipid_contamination (dominant at TE<35 ms) and partial_volume_csf. Joint Hadamard well-posedness for the coupled MRS + metabolite-ratio threshold forward established by Howe 2003, Tate 2003, McKnight 2002, Kurhanewicz 2002 (prostate), Bolan 2003 (breast), Provencher 1993 (LCModel).
Solvability C
- Solver class:
- linear-operator + convex optimisation [LCModel / jMRUI / TARQUIN metabolite quantification] + categorical-readout [organ-specific ratio threshold] | end-to-end deep neural [DeepMRS]
- Convergence rate q:
- 2
- Complexity:
- O(H_voxels * W_voxels * Z_voxels * spectral_points) for stage 1; O(H W Z * N_grades) for stage 2