Susceptibility-Weighted Imaging (SWI)
swi
Medical
Spin/rf
Forward-Model Signal Chain
Each primitive represents a physical operation in the measurement process. Arrows show signal flow left to right.
Spec Notation
M → F → S → D
Benchmark Variants & Leaderboards
Susceptibility-Weighted Imaging (SWI)
Susceptibility-Weighted Imaging (SWI)
Spec Notation
M → F → S → D
Standard Leaderboard (Top 10)
| # | Method | Score | PSNR (dB) | SSIM | Trust | Source |
|---|---|---|---|---|---|---|
| 🥇 | SwinMR++ | 0.971 | 43.8 | 0.983 | ✓ Certified | Huang et al., IEEE TMI 2025 — 5 improvements: multi-scale axial attention (cross-scale long-range modeling), INR coordinate-query head (high-acceleration k-space interpolation), k-space DC per unrolled module, joint LPIPS+SSIM+k-space consistency loss, dynamic conv-Transformer branch weighting |
| 🥈 | HUMUS-Net++ | 0.958 | 43.1 | 0.979 | ✓ Certified | Fabian et al., dHUMUS-Net 2023 — 5 improvements: k-space DC per unrolled module, dynamic optimal-scale prediction (dHUMUS-Net), INR coordinate head (continuous representation), LPIPS+SSIM perceptual-structural loss, lightweight axial attention Transformer |
| 🥉 | MR-IPT | 0.950 | 42.48 | 0.983 | ✓ Certified | Sci. Reports 2025 |
| 4 | HybridCascade++ | 0.949 | 42.5 | 0.981 | ✓ Certified | HybridCascade++ MICCAI 2021 + IEEE TMI 2025 — 5 improvements: multi-stage cascade DC (coarse-to-fine 4-stage unrolling), SIREN INR warm-start (continuous prior initialization), SSIM structural anchor (perceptual consistency in late DC stages), DRUNet final polish (blind denoising post-DC), freq-blend LF/HF fusion (SIREN low-freq + structured high-freq recombination) |
| 5 | MoDL-Net++ | 0.936 | 41.8 | 0.978 | ✓ Certified | MoDL-Net++ IEEE TMI 2025 — 5 improvements: multi-scale pyramid fusion (coarse-to-fine representation), RDN/Swin deep prior (rich feature hierarchy), differentiable DC layers (physics-informed unrolling), joint LPIPS+SSIM+L1 loss (perceptual+structural+fidelity), two-stage training (pre-train then fine-tune with DC) |
| 6 | U-Net++ | 0.931 | 41.5 | 0.978 | ✓ Certified | Chen & Boning, IEEE TMI 2024 — 5 improvements: Residual U-Net blocks (dense skip connections), data consistency layers (physics-informed k-space projection), plug-and-play prior (learned denoiser as proximal operator), joint SSIM+MSE+DC loss, multi-scale feature aggregation |
| 7 | MRI-FM | 0.926 | 42.1 | 0.948 | ✓ Certified | Wang et al., Nature MI 2026 |
| 8 | ReconFormer++ | 0.926 | 41.5 | 0.969 | ✓ Certified | Pan et al., IEEE TMI 2025 |
| 9 | PromptMR-SFM | 0.924 | 41.3 | 0.971 | ✓ Certified | PWM 2026 |
| 10 | PnP-DnCNN-Pro | 0.917 | 41.0 | 0.968 | ✓ Certified | PnP-DnCNN-Pro IEEE TMI 2025 (DOI:10.1109/TMI.2025.3441240) — 5 improvements: multi-scale DnCNN denoiser (SwinIR-style hierarchical feature extraction), adaptive mu/sigma schedule (dynamic regularization per PnP iteration), SIREN INR coordinate output head (continuous representation for high-acceleration interpolation), joint LPIPS+SSIM denoiser training (perceptual+structural loss), dynamic PnP regularization scheduling (learnable lambda per iteration) |
Showing top 10 of 35 methods. View all →
Mismatch Parameters (3) click to expand
| Name | Symbol | Description | Nominal | Perturbed |
|---|---|---|---|---|
| phase_unwrapping_error | p_u | Phase unwrapping error (-) | 0.0 | 1.0 |
| background_field_removal_error | b_f | Background field removal error (-) | 0.0 | 2.0 |
| dipole_inversion_regularization | d_i | Dipole inversion regularization (-) | 0.0 | 0.0 |