Hidden
Neutron Tomo — 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 |
|---|---|---|
| beam_spectrum | -2.1 – 6.9 | % |
| scatter_correction | -3.5 – 11.5 | % |
| rotation_offset | -0.7 – 2.3 | pixels |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | PETFormer + gradient | 0.666 | 26.06 | 0.834 | 0.86 | ✓ Certified | Li et al., ECCV 2024 |
| 2 | FBP-PET + gradient | 0.596 | 23.92 | 0.766 | 0.75 | ✓ Certified | Analytical baseline |
| 3 | TransEM + gradient | 0.560 | 22.45 | 0.709 | 0.76 | ✓ Certified | Xie et al., 2023 |
| 4 | U-Net-PET + gradient | 0.526 | 21.08 | 0.649 | 0.78 | ✓ Certified | Ronneberger et al. variant, MICCAI 2020 |
| 5 | DeepPET + gradient | 0.520 | 21.07 | 0.649 | 0.75 | ✓ Certified | Haggstrom et al., MIA 2019 |
| 6 | ML-EM + gradient | 0.519 | 20.4 | 0.618 | 0.84 | ✓ Certified | Shepp & Vardi, IEEE TPAMI 1982 |
| 7 | PET-ViT + gradient | 0.491 | 19.24 | 0.562 | 0.87 | ✓ Certified | Smith et al., ICCV 2024 |
| 8 | OS-EM + gradient | 0.472 | 19.15 | 0.557 | 0.79 | ✓ Certified | Hudson & Larkin, IEEE TMI 1994 |
| 9 | OSEM + gradient | 0.471 | 18.7 | 0.535 | 0.85 | ✓ Certified | Hudson & Larkin, IEEE TMI 1994 |
| 10 | MAPEM-RDP + gradient | 0.465 | 18.45 | 0.522 | 0.86 | ✓ Certified | Nuyts et al., IEEE TMI 2002 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%