Public

Spectral CT — Public Tier

(3 scenes)

Full-access development tier with all data visible.

What you get

Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.

How to use

Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.

What to submit

Reconstructed signals (x_hat) and corrected spec as HDF5.

Parameter Specifications

True spec visible — use these exact values for Scenario III oracle reconstruction.

Parameter Spec Range True Value Unit
energy_calibration_error -4.0 – 8.0 2.0 keV
scatter_fraction -0.2 – 0.4 0.1
detector_crosstalk -0.1 – 0.2 0.05
beam_hardening -0.2 – 0.4 0.1

InverseNet Baseline Scores

Method: CPU_baseline — Mismatch parameter: nominal

Scenario I (Ideal)

14.88 dB

SSIM 0.2885

Scenario II (Mismatch)

11.68 dB

SSIM 0.0479

Scenario III (Oracle)

14.89 dB

SSIM 0.1290

Per-scene breakdown (4 scenes)
Scene PSNR I SSIM I PSNR II SSIM II PSNR III SSIM III
scene_00 14.80 0.2898 11.69 0.0458 14.77 0.1268
scene_01 15.05 0.2857 11.70 0.0477 14.91 0.1282
scene_02 14.99 0.2846 11.68 0.0506 14.96 0.1312
scene_03 14.69 0.2938 11.67 0.0475 14.91 0.1299

Public Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 CTFormer + gradient 0.859 38.05 0.982 0.93 ✓ Certified Li et al., ICCV 2024
2 DiffusionCT + gradient 0.843 37.33 0.979 0.89 ✓ Certified Kazemi et al., ECCV 2024
3 Score-CT + gradient 0.843 36.97 0.978 0.91 ✓ Certified Song et al., NeurIPS 2024
4 DuDoTrans + gradient 0.838 36.03 0.974 0.94 ✓ Certified Wang et al., MLMIR 2022
5 CT-ViT + gradient 0.835 36.26 0.975 0.91 ✓ Certified Guo et al., NeurIPS 2024
6 DOLCE + gradient 0.828 36.4 0.975 0.87 ✓ Certified Liu et al., ICCV 2023
7 Learned Primal-Dual + gradient 0.822 34.78 0.966 0.94 ✓ Certified Adler & Oktem, IEEE TMI 2018
8 FBPConvNet + gradient 0.794 33.4 0.956 0.89 ✓ Certified Jin et al., IEEE TIP 2017
9 PnP-DnCNN + gradient 0.785 32.29 0.946 0.92 ✓ Certified Zhang et al., IEEE TIP 2017
10 RED-CNN + gradient 0.785 32.17 0.944 0.93 ✓ Certified Chen et al., IEEE TMI 2017
11 PnP-ADMM + gradient 0.750 30.43 0.923 0.88 ✓ Certified Venkatakrishnan et al., IEEE GlobalSIP 2013
12 TV-ADMM + gradient 0.706 27.78 0.876 0.89 ✓ Certified Sidky et al., Phys. Med. Biol. 2008
13 FBP + gradient 0.644 24.71 0.793 0.9 ✓ Certified Kak & Slaney, IEEE Press 1988

Visible Data Fields

y H_ideal spec_ranges x_true true_spec

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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