Hidden

SPECT — 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
center_offset -1.05 – 3.45 pixels
collimator_septal -0.014 – 0.046
attenuation -3.5 – 11.5 %
scatter 0.165 – 0.315

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 PET-ViT + gradient 0.730 29.72 0.912 0.84 ✓ Certified Smith et al., ICCV 2024
2 PETFormer + gradient 0.704 27.87 0.878 0.87 ✓ Certified Li et al., ECCV 2024
3 FBP-PET + gradient 0.680 26.63 0.849 0.87 ✓ Certified Analytical baseline
4 ML-EM + gradient 0.633 24.65 0.791 0.85 ✓ Certified Shepp & Vardi, IEEE TPAMI 1982
5 DeepPET + gradient 0.618 24.59 0.789 0.78 ✓ Certified Haggstrom et al., MIA 2019
6 TransEM + gradient 0.612 23.77 0.76 0.85 ✓ Certified Xie et al., 2023
7 U-Net-PET + gradient 0.606 23.76 0.76 0.82 ✓ Certified Ronneberger et al. variant, MICCAI 2020
8 OS-EM + gradient 0.588 23.38 0.746 0.78 ✓ Certified Hudson & Larkin, IEEE TMI 1994
9 MAPEM-RDP + gradient 0.588 23.44 0.748 0.77 ✓ Certified Nuyts et al., IEEE TMI 2002
10 OSEM + gradient 0.508 20.52 0.623 0.77 ✓ Certified Hudson & Larkin, IEEE TMI 1994

Dataset

Scenes: 3

Scoring Formula

0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

PSNR: 40% SSIM: 40% Consistency: 20%
Back to SPECT