Hidden
MR Fingerprinting (MRF) — Hidden Tier
(3 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| dictionary_resolution_(t1,_t2) | -0.7 – 2.3 | - |
| b1_inhomogeneity | -2.1 – 6.9 | - |
| undersampling_artifact | -2.8 – 9.2 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | MRF-Former + gradient | 0.667 | 26.67 | 0.85 | 0.8 | ✓ Certified | MRF tissue quantification transformer, 2024 |
| 2 | MANTIS + gradient | 0.572 | 22.91 | 0.728 | 0.76 | ✓ Certified | Cohen et al., MRM 2018 |
| 3 | SVD-MRF + gradient | 0.489 | 19.5 | 0.574 | 0.82 | ✓ Certified | Ma et al., Nature 2013 |
| 4 | MRF-Net + gradient | 0.450 | 18.46 | 0.523 | 0.78 | ✓ Certified | Cohen et al., Med. Phys. 2018 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%