Dev

Proton Therapy Imaging — 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
range_uncertainty -0.72 – 1.08 mm
scattering_power_error 0.988 – 1.018 -
detector_efficiency_drift 0.838 – 0.868 -
setup_error -0.48 – 0.72 mm

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 CT-ViT + gradient 0.803 34.18 0.962 0.88 ✓ Certified Guo et al., NeurIPS 2024
2 CTFormer + gradient 0.797 34.86 0.967 0.81 ✓ Certified Li et al., ICCV 2024
3 Score-CT + gradient 0.745 30.81 0.928 0.83 ✓ Certified Song et al., NeurIPS 2024
4 Learned Primal-Dual + gradient 0.740 29.71 0.912 0.89 ✓ Certified Adler & Oktem, IEEE TMI 2018
5 DOLCE + gradient 0.726 29.57 0.91 0.83 ✓ Certified Liu et al., ICCV 2023
6 PnP-ADMM + gradient 0.721 28.57 0.892 0.89 ✓ Certified Venkatakrishnan et al., IEEE GlobalSIP 2013
7 DiffusionCT + gradient 0.718 29.48 0.909 0.8 ✓ Certified Kazemi et al., ECCV 2024
8 DuDoTrans + gradient 0.713 29.29 0.905 0.79 ✓ Certified Wang et al., MLMIR 2022
9 PnP-DnCNN + gradient 0.702 27.54 0.871 0.89 ✓ Certified Zhang et al., IEEE TIP 2017
10 FBPConvNet + gradient 0.700 28.21 0.885 0.82 ✓ Certified Jin et al., IEEE TIP 2017
11 TV-ADMM + gradient 0.698 27.43 0.868 0.88 ✓ Certified Sidky et al., Phys. Med. Biol. 2008
12 RED-CNN + gradient 0.679 27.39 0.867 0.79 ✓ Certified Chen et al., IEEE TMI 2017
13 FBP + gradient 0.658 25.95 0.831 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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