Dev

PET/CT — 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
ct_registration_shift -4.8 – 7.2 pixels
hu_to_mu_scale -12.0 – 18.0 %
scatter_fraction -0.18 – 0.27

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 CTFormer + gradient 0.801 35.3 0.97 0.8 ✓ Certified Li et al., ICCV 2024
2 CT-ViT + gradient 0.800 34.44 0.964 0.85 ✓ Certified Guo et al., NeurIPS 2024
3 DiffusionCT + gradient 0.753 30.78 0.928 0.87 ✓ Certified Kazemi et al., ECCV 2024
4 Score-CT + gradient 0.739 29.54 0.91 0.9 ✓ Certified Song et al., NeurIPS 2024
5 DuDoTrans + gradient 0.720 28.88 0.898 0.86 ✓ Certified Wang et al., MLMIR 2022
6 Learned Primal-Dual + gradient 0.698 27.79 0.876 0.85 ✓ Certified Adler & Oktem, IEEE TMI 2018
7 DOLCE + gradient 0.692 27.32 0.866 0.86 ✓ Certified Liu et al., ICCV 2023
8 PnP-DnCNN + gradient 0.692 27.98 0.88 0.8 ✓ Certified Zhang et al., IEEE TIP 2017
9 PnP-ADMM + gradient 0.672 25.96 0.831 0.9 ✓ Certified Venkatakrishnan et al., IEEE GlobalSIP 2013
10 FBPConvNet + gradient 0.670 26.55 0.847 0.83 ✓ Certified Jin et al., IEEE TIP 2017
11 FBP + gradient 0.668 26.25 0.839 0.85 ✓ Certified Kak & Slaney, IEEE Press 1988
12 TV-ADMM + gradient 0.662 25.75 0.825 0.87 ✓ Certified Sidky et al., Phys. Med. Biol. 2008
13 RED-CNN + gradient 0.602 23.24 0.74 0.87 ✓ Certified Chen et al., IEEE TMI 2017

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%
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