Hidden

Elastography — Hidden Tier

(3 scenes)

Fully blind server-side evaluation — no data download.

What you get

No data downloadable. Algorithm runs server-side on hidden measurements.

How to use

Package algorithm as Docker container / Python script. Submit via link.

What to submit

Containerized algorithm accepting y + H, outputting x_hat + corrected spec.

Parameter Specifications

🔒

True spec hidden — blind evaluation, only ranges available.

Parameter Spec Range Unit
shear_speed -0.21 – 0.69 m/s
push_duration -7.0 – 23.0 μs
tissue_viscosity -10.5 – 34.5 %

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 SwinElasto + gradient 0.730 29.97 0.916 0.82 ✓ Certified Wang et al., IEEE TMI 2023
2 DiffElasto + gradient 0.729 30.49 0.924 0.77 ✓ Certified Gao et al., MICCAI 2024
3 PhysElasto + gradient 0.694 28.68 0.894 0.75 ✓ Certified Chen et al., Magn. Reson. Med. 2024
4 TransElasto + gradient 0.689 28.03 0.881 0.78 ✓ Certified Li et al., Magn. Reson. Med. 2022
5 ElastoNet + gradient 0.611 24.5 0.786 0.76 ✓ Certified Tzschatzsch et al., IEEE TMI 2021
6 DnCNN-Elasto + gradient 0.522 20.57 0.626 0.83 ✓ Certified Guo et al., Med. Phys. 2019
7 LFE-Elasto + gradient 0.488 19.83 0.59 0.77 ✓ Certified Manduca et al., Magn. Reson. Imaging 2001
8 AIDE + gradient 0.326 14.28 0.322 0.77 ✓ Certified Oliphant et al., Magn. Reson. Med. 2001
9 DI-Elasto + gradient 0.305 13.3 0.281 0.8 ✓ Certified Van Houten et al., Magn. Reson. Med. 2001

Dataset

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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