Dev
Muon Tomo — 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 |
|---|---|---|
| angular_resolution | -2.4 – 3.6 | mrad |
| momentum_estimate | -12.0 – 18.0 | % |
| detector_efficiency | -3.6 – 5.4 | % |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | TransEM + gradient | 0.729 | 29.62 | 0.911 | 0.84 | ✓ Certified | Xie et al., 2023 |
| 2 | PETFormer + gradient | 0.716 | 28.42 | 0.889 | 0.88 | ✓ Certified | Li et al., ECCV 2024 |
| 3 | PET-ViT + gradient | 0.676 | 27.05 | 0.859 | 0.81 | ✓ Certified | Smith et al., ICCV 2024 |
| 4 | DeepPET + gradient | 0.643 | 25.46 | 0.816 | 0.81 | ✓ Certified | Haggstrom et al., MIA 2019 |
| 5 | MAPEM-RDP + gradient | 0.619 | 24.5 | 0.786 | 0.8 | ✓ Certified | Nuyts et al., IEEE TMI 2002 |
| 6 | FBP-PET + gradient | 0.616 | 24.18 | 0.775 | 0.82 | ✓ Certified | Analytical baseline |
| 7 | ML-EM + gradient | 0.595 | 22.86 | 0.726 | 0.88 | ✓ Certified | Shepp & Vardi, IEEE TPAMI 1982 |
| 8 | OSEM + gradient | 0.577 | 22.62 | 0.716 | 0.82 | ✓ Certified | Hudson & Larkin, IEEE TMI 1994 |
| 9 | U-Net-PET + gradient | 0.567 | 21.74 | 0.679 | 0.89 | ✓ Certified | Ronneberger et al. variant, MICCAI 2020 |
| 10 | OS-EM + gradient | 0.540 | 21.11 | 0.651 | 0.84 | ✓ Certified | Hudson & Larkin, IEEE TMI 1994 |
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%