Dev
MR Fingerprinting (MRF) — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| dictionary_resolution_(t1,_t2) | -1.2 – 1.8 | - |
| b1_inhomogeneity | -3.6 – 5.4 | - |
| undersampling_artifact | -4.8 – 7.2 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | MRF-Former + gradient | 0.703 | 28.6 | 0.893 | 0.8 | ✓ Certified | MRF tissue quantification transformer, 2024 |
| 2 | MANTIS + gradient | 0.612 | 24.17 | 0.775 | 0.8 | ✓ Certified | Cohen et al., MRM 2018 |
| 3 | MRF-Net + gradient | 0.519 | 20.19 | 0.608 | 0.87 | ✓ Certified | Cohen et al., Med. Phys. 2018 |
| 4 | SVD-MRF + gradient | 0.516 | 20.45 | 0.62 | 0.82 | ✓ Certified | Ma et al., Nature 2013 |
Visible Data Fields
y
H_ideal
spec_ranges
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%