Hidden

Arterial Spin Labeling (ASL) MRI — 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
labeling_efficiency 0.836 – 0.896 -
transit_delay 1.29 – 2.19 s
t1_blood_error -1.4 – 4.6 -

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Score-MRI (ASL) + gradient 0.713 29.27 0.905 0.79 ✓ Certified Chung & Ye, Med. Image Anal. 93:102689, 2022
2 ReconFormer + gradient 0.660 26.34 0.841 0.8 ✓ Certified Guo et al., IEEE TMI 41(5):1297, 2024
3 PromptMR + gradient 0.654 25.68 0.823 0.84 ✓ Certified Xin et al., ECCV 2024
4 U-Net (ASL) + gradient 0.622 24.45 0.784 0.82 ✓ Certified Tian et al., MRM 89(4):1616, 2023
5 E2E-VarNet + gradient 0.621 24.3 0.779 0.83 ✓ Certified Sriram et al., MICCAI 2020
6 PnP-DnCNN + gradient 0.620 24.12 0.773 0.85 ✓ Certified Ahmad et al., IEEE SPM 2020
7 Kinetic-CS + gradient 0.526 20.72 0.633 0.83 ✓ Certified Zhao et al., JMRI 60(4):1204, 2024
8 Zero-Filled IFFT + gradient 0.485 19.84 0.591 0.75 ✓ Certified Zbontar et al., fastMRI, arXiv 2018
9 L1-Wavelet (ESPIRiT) + gradient 0.376 15.44 0.375 0.86 ✓ Certified Lustig et al., MRM 2007; Uecker et al., MRM 2014

Dataset

Scenes: 3

Scoring Formula

0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

PSNR: 40% SSIM: 40% Consistency: 20%
Back to Arterial Spin Labeling (ASL) MRI