Dev

Arterial Spin Labeling (ASL) MRI — 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
labeling_efficiency 0.826 – 0.886 -
transit_delay 1.14 – 2.04 s
t1_blood_error -2.4 – 3.6 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Score-MRI (ASL) + gradient 0.729 29.29 0.905 0.87 ✓ Certified Chung & Ye, Med. Image Anal. 93:102689, 2022
2 PromptMR + gradient 0.724 29.22 0.904 0.85 ✓ Certified Xin et al., ECCV 2024
3 ReconFormer + gradient 0.714 29.19 0.904 0.8 ✓ Certified Guo et al., IEEE TMI 41(5):1297, 2024
4 E2E-VarNet + gradient 0.703 28.01 0.881 0.85 ✓ Certified Sriram et al., MICCAI 2020
5 PnP-DnCNN + gradient 0.656 25.51 0.818 0.87 ✓ Certified Ahmad et al., IEEE SPM 2020
6 U-Net (ASL) + gradient 0.648 25.03 0.803 0.88 ✓ Certified Tian et al., MRM 89(4):1616, 2023
7 Kinetic-CS + gradient 0.600 23.88 0.764 0.78 ✓ Certified Zhao et al., JMRI 60(4):1204, 2024
8 Zero-Filled IFFT + gradient 0.551 21.09 0.65 0.9 ✓ Certified Zbontar et al., fastMRI, arXiv 2018
9 L1-Wavelet (ESPIRiT) + gradient 0.468 18.32 0.516 0.89 ✓ Certified Lustig et al., MRM 2007; Uecker et al., MRM 2014

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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