Public

Fluoroscopy — 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
motion_blur -5.0 – 10.0 2.5 ms
lag -3.0 – 6.0 1.5 ms
gain_drift -0.5 – 1.0 0.25 %

InverseNet Baseline Scores

Method: CPU_baseline — Mismatch parameter: nominal

Scenario I (Ideal)

14.88 dB

SSIM 0.2885

Scenario II (Mismatch)

11.40 dB

SSIM 0.0410

Scenario III (Oracle)

14.64 dB

SSIM 0.1177

Per-scene breakdown (4 scenes)
Scene PSNR I SSIM I PSNR II SSIM II PSNR III SSIM III
scene_00 14.80 0.2898 11.38 0.0400 14.58 0.1169
scene_01 15.05 0.2857 11.41 0.0409 14.66 0.1169
scene_02 14.99 0.2846 11.40 0.0427 14.66 0.1192
scene_03 14.69 0.2938 11.40 0.0405 14.65 0.1180

Public Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 DiffFluoro + gradient 0.847 37.61 0.981 0.89 ✓ Certified Gao et al., MICCAI 2024
2 PhysFluoro + gradient 0.831 36.33 0.975 0.89 ✓ Certified Chen et al., IEEE TMI 2024
3 SwinFluoro + gradient 0.816 34.73 0.966 0.91 ✓ Certified Li et al., Med. Phys. 2023
4 TransFluoro + gradient 0.802 34.27 0.963 0.87 ✓ Certified Wang et al., IEEE TMI 2022
5 REDCNN-Fluoro + gradient 0.771 32.02 0.943 0.87 ✓ Certified Chen et al., IEEE TMI 2017
6 DnCNN-Fluoro + gradient 0.763 30.4 0.923 0.95 ✓ Certified Chen et al., IEEE TMI 2017
7 TV-Fluoro + gradient 0.694 27.13 0.861 0.89 ✓ Certified Sidky & Pan, Phys. Med. Biol. 2008
8 NLM-Fluoro + gradient 0.650 25.14 0.807 0.88 ✓ Certified Buades et al., CVPR 2005
9 BM3D-Fluoro + gradient 0.610 23.4 0.746 0.89 ✓ Certified Dabov et al., IEEE TIP 2007

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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