Hidden
Diffusion 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 |
|---|---|---|
| b_value_error | -2.1 – 6.9 | % |
| eddy_current | -0.35 – 1.15 | voxels |
| gradient_direction | -0.7 – 2.3 | deg |
| susceptibility | -0.7 – 2.3 | voxels |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DiffusionDTI + gradient | 0.724 | 29.08 | 0.902 | 0.86 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 2 | PhysDiffMRI + gradient | 0.696 | 27.77 | 0.876 | 0.84 | ✓ Certified | Chen et al., MRM 2024 |
| 3 | DTIFormer + gradient | 0.672 | 27.23 | 0.864 | 0.77 | ✓ Certified | Liu et al., MICCAI 2022 |
| 4 | SwinDTI + gradient | 0.650 | 26.34 | 0.841 | 0.75 | ✓ Certified | Wang et al., MICCAI 2023 |
| 5 | DWIML-Net + gradient | 0.588 | 23.08 | 0.734 | 0.82 | ✓ Certified | Qin et al., IEEE TMI 2019 |
| 6 | SHORE + gradient | 0.536 | 21.5 | 0.668 | 0.77 | ✓ Certified | Merlet & Deriche, MRM 2013 |
| 7 | DnCNN-DTI + gradient | 0.516 | 20.08 | 0.603 | 0.87 | ✓ Certified | Golkov et al., IEEE TMI 2016 |
| 8 | DTI-FIT + gradient | 0.434 | 17.99 | 0.5 | 0.77 | ✓ Certified | Behrens et al., MRM 2003 |
| 9 | CHARMED + gradient | 0.397 | 16.48 | 0.425 | 0.81 | ✓ Certified | Assaf & Basser, NeuroImage 2005 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%