Dev
PET/MR — 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 |
|---|---|---|
| mr_attenuation_error | -30.0 – 45.0 | % |
| motion_shift | -7.2 – 10.8 | pixels |
| b0_inhomogeneity | -48.0 – 72.0 | Hz |
| pet_scatter_fraction | -0.3 – 0.45 |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | PET-ViT + gradient | 0.769 | 32.81 | 0.951 | 0.8 | ✓ Certified | Smith et al., ICCV 2024 |
| 2 | PETFormer + gradient | 0.732 | 30.07 | 0.918 | 0.82 | ✓ Certified | Li et al., ECCV 2024 |
| 3 | U-Net-PET + gradient | 0.702 | 28.1 | 0.883 | 0.84 | ✓ Certified | Ronneberger et al. variant, MICCAI 2020 |
| 4 | TransEM + gradient | 0.701 | 27.74 | 0.875 | 0.87 | ✓ Certified | Xie et al., 2023 |
| 5 | FBP-PET + gradient | 0.701 | 28.23 | 0.886 | 0.82 | ✓ Certified | Analytical baseline |
| 6 | ML-EM + gradient | 0.676 | 26.76 | 0.852 | 0.84 | ✓ Certified | Shepp & Vardi, IEEE TPAMI 1982 |
| 7 | OS-EM + gradient | 0.639 | 25.27 | 0.811 | 0.81 | ✓ Certified | Hudson & Larkin, IEEE TMI 1994 |
| 8 | DeepPET + gradient | 0.613 | 24.08 | 0.771 | 0.82 | ✓ Certified | Haggstrom et al., MIA 2019 |
| 9 | OSEM + gradient | 0.593 | 23.1 | 0.735 | 0.84 | ✓ Certified | Hudson & Larkin, IEEE TMI 1994 |
| 10 | MAPEM-RDP + gradient | 0.582 | 22.67 | 0.718 | 0.84 | ✓ Certified | Nuyts et al., IEEE TMI 2002 |
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%