Dev

Portal Imaging (EPID) — 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
isocenter_shift -0.48 – 0.72 mm
beam_energy_variation 5.952 – 6.072 MV
detector_sag -0.24 – 0.36 mm
scatter_kernel_width 4.52 – 5.72 mm

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 CTFormer + gradient 0.790 33.38 0.956 0.87 ✓ Certified Li et al., ICCV 2024
2 Score-CT + gradient 0.784 33.2 0.954 0.85 ✓ Certified Song et al., NeurIPS 2024
3 DuDoTrans + gradient 0.761 31.15 0.933 0.88 ✓ Certified Wang et al., MLMIR 2022
4 CT-ViT + gradient 0.758 32.08 0.944 0.8 ✓ Certified Guo et al., NeurIPS 2024
5 PnP-DnCNN + gradient 0.731 29.18 0.903 0.89 ✓ Certified Zhang et al., IEEE TIP 2017
6 DiffusionCT + gradient 0.719 29.73 0.913 0.78 ✓ Certified Kazemi et al., ECCV 2024
7 Learned Primal-Dual + gradient 0.714 29.08 0.902 0.81 ✓ Certified Adler & Oktem, IEEE TMI 2018
8 FBPConvNet + gradient 0.712 28.39 0.889 0.86 ✓ Certified Jin et al., IEEE TIP 2017
9 DOLCE + gradient 0.698 27.43 0.868 0.88 ✓ Certified Liu et al., ICCV 2023
10 PnP-ADMM + gradient 0.697 28.38 0.889 0.79 ✓ Certified Venkatakrishnan et al., IEEE GlobalSIP 2013
11 TV-ADMM + gradient 0.695 27.61 0.872 0.85 ✓ Certified Sidky et al., Phys. Med. Biol. 2008
12 RED-CNN + gradient 0.673 26.18 0.837 0.88 ✓ Certified Chen et al., IEEE TMI 2017
13 FBP + gradient 0.633 24.83 0.797 0.83 ✓ Certified Kak & Slaney, IEEE Press 1988

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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