Public
MR Fingerprinting (MRF) — Public Tier
(3 scenes)Full-access development tier with all data visible.
What you get
Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.
How to use
Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.
What to submit
Reconstructed signals (x_hat) and corrected spec as HDF5.
Parameter Specifications
✓
True spec visible — use these exact values for Scenario III oracle reconstruction.
| Parameter | Spec Range | True Value | Unit |
|---|---|---|---|
| dictionary_resolution_(t1,_t2) | -1.0 – 2.0 | 0.5 | - |
| b1_inhomogeneity | -3.0 – 6.0 | 1.5 | - |
| undersampling_artifact | -4.0 – 8.0 | 2.0 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
17.61 dB
SSIM 0.3262
Scenario II (Mismatch)
13.39 dB
SSIM 0.0553
Scenario III (Oracle)
16.66 dB
SSIM 0.1359
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 16.58 | 0.3251 | 12.85 | 0.0515 | 16.16 | 0.1317 |
| scene_01 | 18.88 | 0.3252 | 14.12 | 0.0562 | 17.37 | 0.1299 |
| scene_02 | 18.30 | 0.3298 | 13.74 | 0.0603 | 16.95 | 0.1425 |
| scene_03 | 16.68 | 0.3248 | 12.85 | 0.0534 | 16.15 | 0.1394 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | MRF-Former + gradient | 0.785 | 32.3 | 0.946 | 0.92 | ✓ Certified | MRF tissue quantification transformer, 2024 |
| 2 | MRF-Net + gradient | 0.758 | 30.46 | 0.924 | 0.92 | ✓ Certified | Cohen et al., Med. Phys. 2018 |
| 3 | MANTIS + gradient | 0.643 | 24.94 | 0.8 | 0.87 | ✓ Certified | Cohen et al., MRM 2018 |
| 4 | SVD-MRF + gradient | 0.542 | 20.76 | 0.635 | 0.9 | ✓ Certified | Ma et al., Nature 2013 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
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%