Hidden
Susceptibility-Weighted Imaging (SWI) — 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 |
|---|---|---|
| phase_unwrapping_error | -0.7 – 2.3 | - |
| background_field_removal_error | -1.4 – 4.6 | - |
| dipole_inversion_regularization | -0.15 – 0.15 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | ReconFormer++ + gradient | 0.822 | 35.76 | 0.972 | 0.88 | ✓ Certified | Pan et al., IEEE TMI 2025 |
| 2 | HUMUS-Net++ + gradient | 0.817 | 36.79 | 0.977 | 0.79 | ✓ Certified | Fabian et al., dHUMUS-Net 2023 — k-space DC per module + dynamic multi-scale weighting + INR head + perceptual-structural loss + axial attention |
| 3 | SwinMR++ + gradient | 0.787 | 35.18 | 0.969 | 0.74 | ✓ Certified | Huang et al., IEEE TMI 2025 — multi-scale axial attention + INR head + k-space DC per module + LPIPS+SSIM+k-space joint loss + dynamic feature fusion |
| 4 | PnP-DnCNN-Pro + gradient | 0.777 | 32.73 | 0.95 | 0.85 | ✓ Certified | PnP-DnCNN-Pro IEEE TMI 2025 (DOI:10.1109/TMI.2025.3441240) — multi-scale DnCNN denoiser + adaptive mu/sigma schedule + SIREN INR output head + joint LPIPS+SSIM denoiser training + dynamic PnP regularization scheduling |
| 5 | SwinMR + gradient | 0.777 | 32.46 | 0.947 | 0.87 | ✓ Certified | Huang et al., MICCAI 2022 |
| 6 | HybridCascade++ + gradient | 0.775 | 32.97 | 0.952 | 0.82 | ✓ Certified | HybridCascade++ MICCAI 2021 + IEEE TMI 2025 — multi-scale cascade DC + SIREN INR warm-start + SSIM structural anchor + DRUNet polish + freq-blend LF/HF fusion |
| 7 | ReconFormer + gradient | 0.773 | 33.01 | 0.953 | 0.81 | ✓ Certified | Guo et al., IEEE TMI 2024 |
| 8 | PromptMR-SFM + gradient | 0.769 | 32.27 | 0.946 | 0.84 | ✓ Certified | PWM 2026 |
| 9 | MRI-DiffusionNet + gradient | 0.766 | 32.78 | 0.951 | 0.79 | ✓ Certified | Song et al., ICCV 2024 |
| 10 | MoDL-Net++ + gradient | 0.762 | 32.21 | 0.945 | 0.81 | ✓ Certified | MoDL-Net++ IEEE TMI 2025 — multi-scale pyramid fusion + RDN/Swin deep prior + differentiable DC layers + LPIPS+SSIM+L1 joint loss + two-stage training strategy |
| 11 | MR-IPT + gradient | 0.754 | 30.68 | 0.927 | 0.88 | ✓ Certified | Sci. Reports 2025 |
| 12 | MRI-FM + gradient | 0.752 | 31.08 | 0.932 | 0.84 | ✓ Certified | Wang et al., Nature MI 2026 |
| 13 | MRDynamo + gradient | 0.752 | 30.68 | 0.927 | 0.87 | ✓ Certified | Chen et al., NeurIPS 2024 |
| 14 | U-Net++ + gradient | 0.746 | 31.8 | 0.94 | 0.76 | ✓ Certified | Chen & Boning, IEEE TMI 2024 (DOI: 10.1109/TMI.2024.3367890) — Residual U-Net + data consistency layers + plug-and-play prior + residual connections + dense skip paths |
| 15 | HUMUS-Net + gradient | 0.742 | 31.25 | 0.934 | 0.78 | ✓ Certified | Fabian et al., NeurIPS 2022 |
| 16 | BrainID-MRI + gradient | 0.732 | 29.55 | 0.91 | 0.86 | ✓ Certified | Liu et al., CVPR 2025 |
| 17 | E2E-VarNet + gradient | 0.729 | 29.54 | 0.91 | 0.85 | ✓ Certified | Sriram et al., MICCAI 2020 |
| 18 | PnP-DnCNN + gradient | 0.724 | 29.0 | 0.9 | 0.87 | ✓ Certified | Ahmad et al., IEEE TCI 2019 (DOI:10.1109/TCI.2019.2944521) |
| 19 | MMR-Mamba + gradient | 0.717 | 29.27 | 0.905 | 0.81 | ✓ Certified | Zhao et al., Med. Image Anal. 2025 |
| 20 | MoDL + gradient | 0.691 | 28.5 | 0.891 | 0.75 | ✓ Certified | Aggarwal et al., IEEE TMI 2019 |
| 21 | BM3D-MRI + gradient | 0.690 | 27.86 | 0.878 | 0.8 | ✓ Certified | Eksioglu, Comput. Math. Meth. Med. 2016 |
| 22 | GRAPPA + gradient | 0.687 | 26.87 | 0.855 | 0.88 | ✓ Certified | Griswold et al., MRM 2002 |
| 23 | DCCNN + gradient | 0.676 | 26.76 | 0.852 | 0.84 | ✓ Certified | Schlemper et al., IEEE TMI 2018 |
| 24 | PromptMR + gradient | 0.675 | 26.28 | 0.84 | 0.88 | ✓ Certified | Bai et al., ECCV 2024 |
| 25 | HybridCascade + gradient | 0.636 | 24.87 | 0.798 | 0.84 | ✓ Certified | Fastmri, arXiv 2020 |
| 26 | Deep-ADMM-Net + gradient | 0.623 | 25.0 | 0.802 | 0.76 | ✓ Certified | Yang et al., NeurIPS 2016 |
| 27 | k-t SPARSE-SENSE + gradient | 0.621 | 24.14 | 0.773 | 0.85 | ✓ Certified | Lustig et al., MRM 2006 |
| 28 | ALOHA + gradient | 0.609 | 24.47 | 0.785 | 0.75 | ✓ Certified | Jin et al., IEEE TMI 2016 |
| 29 | SENSE + gradient | 0.605 | 23.65 | 0.756 | 0.83 | ✓ Certified | Pruessmann et al., MRM 1999 |
| 30 | LORAKS + gradient | 0.596 | 23.87 | 0.764 | 0.76 | ✓ Certified | Haldar, IEEE TMI 2014 |
| 31 | L1-Wavelet + gradient | 0.579 | 22.94 | 0.729 | 0.79 | ✓ Certified | Lustig et al., MRM 2007 |
| 32 | U-Net + gradient | 0.575 | 22.18 | 0.698 | 0.87 | ✓ Certified | Zbontar et al., arXiv 2018 |
| 33 | Score-MRI + gradient | 0.554 | 21.43 | 0.665 | 0.87 | ✓ Certified | Chung & Ye, Med. Image Anal. 2022 |
| 34 | ESPIRiT + gradient | 0.553 | 22.02 | 0.691 | 0.78 | ✓ Certified | Uecker et al., MRM 2014 |
| 35 | Zero-Filled IFFT + gradient | 0.527 | 21.32 | 0.66 | 0.75 | ✓ Certified | Pruessmann et al., MRM 1999 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%