Dev

Magnetic Particle Imaging (MPI) — Dev Tier

(5 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
drive_field_amplitude 24.28 – 26.08 mT
selection_field_gradient 2.38 – 2.68 T/m
particle_relaxation_time 1.76 – 2.36 us
receive_coil_sensitivity 0.964 – 1.054 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 SwinIR + gradient 0.691 27.0 0.858 0.89 ✓ Certified Liang et al., ICCVW 2021
2 ScoreExperimental + gradient 0.676 26.95 0.857 0.82 ✓ Certified Wei et al., 2025
3 Domain-Adapted-CNN + gradient 0.653 25.1 0.805 0.9 ✓ Certified Domain adaptation CNN
4 DiffusionExperimental + gradient 0.641 25.18 0.808 0.83 ✓ Certified Zhang et al., 2024
5 ExpFormer + gradient 0.638 24.63 0.79 0.88 ✓ Certified Experimental science transformer, 2024
6 PnP-RED + gradient 0.627 24.85 0.797 0.8 ✓ Certified Romano et al., IEEE TIP 2017
7 Wiener Filter + gradient 0.627 24.49 0.785 0.84 ✓ Certified Wiener filtering baseline
8 ResUNet + gradient 0.608 23.31 0.743 0.89 ✓ Certified Residual U-Net baseline
9 PnP-ADMM + gradient 0.600 23.87 0.764 0.78 ✓ Certified ADMM + denoiser prior
10 Matched Filter + gradient 0.574 22.35 0.705 0.84 ✓ Certified Optimal linear filter
11 Tikhonov + gradient 0.572 22.36 0.705 0.83 ✓ Certified Tikhonov, Doklady 1963

Visible Data Fields

y H_ideal spec_ranges

Dataset

Format: HDF5
Scenes: 5

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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