Dev
CEST 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 |
|---|---|---|
| b0_inhomogeneity | -12.0 – 18.0 | Hz |
| b1_inhomogeneity | -4.8 – 7.2 | - |
| saturation_power_error | -2.4 – 3.6 | - |
| mt_contamination | -7.2 – 10.8 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | PromptCEST + gradient | 0.770 | 32.52 | 0.948 | 0.83 | ✓ Certified | Liu et al., MRM 2024 |
| 2 | CESTFormer + gradient | 0.755 | 30.94 | 0.93 | 0.87 | ✓ Certified | Wu et al., IEEE TMI 2023 |
| 3 | PINN-CEST + gradient | 0.724 | 28.89 | 0.898 | 0.88 | ✓ Certified | Cohen et al., MRM 2022 |
| 4 | DiffusionCEST + gradient | 0.715 | 29.51 | 0.909 | 0.78 | ✓ Certified | Chen et al., NeurIPS 2024 |
| 5 | U-Net-CEST + gradient | 0.681 | 27.22 | 0.863 | 0.82 | ✓ Certified | Zhao et al., MRM 2021 |
| 6 | WASSR + gradient | 0.661 | 26.3 | 0.84 | 0.81 | ✓ Certified | Kim et al., MRM 2009 |
| 7 | Lorentzian-Fit + gradient | 0.641 | 25.29 | 0.811 | 0.82 | ✓ Certified | Zaiss & Bachert, NMR Biomed. 2013 |
| 8 | DnCNN-CEST + gradient | 0.596 | 23.15 | 0.737 | 0.85 | ✓ Certified | Zhang et al., IEEE TIP 2017 (CEST adapted) |
| 9 | MTR-asym + gradient | 0.578 | 22.08 | 0.693 | 0.9 | ✓ Certified | Zhou et al., Nat. Med. 2003 |
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%