fMRI

Functional MRI (BOLD)

Join the Competition Contribute to Benchmark

Standard reconstruction benchmark — forward model perfectly known, no calibration needed. Score = 0.5 × clip((PSNR−15)/30, 0, 1) + 0.5 × SSIM

#	Method	Score	PSNR (dB)	SSIM	Source
🥇	SwinMR++ SwinMR++ 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 43.8 dB SSIM 0.983 Checkpoint unavailable	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++ HUMUS-Net++ 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 43.1 dB SSIM 0.979 Checkpoint unavailable	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 MR-IPT Sci. Reports 2025 42.48 dB SSIM 0.983 Checkpoint unavailable	0.950	42.48	0.983	✓ Certified	Sci. Reports 2025
4	HybridCascade++ HybridCascade++ 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) 42.5 dB SSIM 0.981 Checkpoint unavailable	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++ MoDL-Net++ 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) 41.8 dB SSIM 0.978 Checkpoint unavailable	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++ U-Net++ 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 41.5 dB SSIM 0.978 Checkpoint unavailable	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 MRI-FM Wang et al., Nature MI 2026 42.1 dB SSIM 0.948 Checkpoint unavailable	0.926	42.1	0.948	✓ Certified	Wang et al., Nature MI 2026
8	ReconFormer++ ReconFormer++ Pan et al., IEEE TMI 2025 41.5 dB SSIM 0.969 Checkpoint unavailable	0.926	41.5	0.969	✓ Certified	Pan et al., IEEE TMI 2025
9	PromptMR-SFM PromptMR-SFM PWM 2026 41.3 dB SSIM 0.971 Checkpoint unavailable	0.924	41.3	0.971	✓ Certified	PWM 2026
10	PnP-DnCNN-Pro PnP-DnCNN-Pro 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) 41.0 dB SSIM 0.968 Try in SpecLab →	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)
11	MMR-Mamba MMR-Mamba Zhao et al., Med. Image Anal. 2025 40.98 dB SSIM 0.969 Checkpoint unavailable	0.917	40.98	0.969	✓ Certified	Zhao et al., Med. Image Anal. 2025
12	BrainID-MRI BrainID-MRI Liu et al., CVPR 2025 41.0 dB SSIM 0.942 Checkpoint unavailable	0.904	41.0	0.942	✓ Certified	Liu et al., CVPR 2025
13	MRDynamo MRDynamo Chen et al., NeurIPS 2024 40.5 dB SSIM 0.938 Checkpoint unavailable	0.894	40.5	0.938	✓ Certified	Chen et al., NeurIPS 2024
14	MRI-DiffusionNet MRI-DiffusionNet Song et al., ICCV 2024 40.1 dB SSIM 0.932 Checkpoint unavailable	0.884	40.1	0.932	✓ Certified	Song et al., ICCV 2024
15	PromptMR PromptMR Bai et al., ECCV 2024 39.7 dB SSIM 0.926 Checkpoint unavailable	0.875	39.7	0.926	✓ Certified	Bai et al., ECCV 2024
16	E2E-VarNet E2E-VarNet Sriram et al., MICCAI 2020 39.4 dB SSIM 0.924 Checkpoint unavailable	0.869	39.4	0.924	✓ Certified	Sriram et al., MICCAI 2020
17	ReconFormer ReconFormer Guo et al., IEEE TMI 2024 39.0 dB SSIM 0.922 Checkpoint unavailable	0.861	39.0	0.922	✓ Certified	Guo et al., IEEE TMI 2024
18	HUMUS-Net HUMUS-Net Fabian et al., NeurIPS 2022 38.9 dB SSIM 0.923 Checkpoint unavailable	0.860	38.9	0.923	✓ Certified	Fabian et al., NeurIPS 2022
19	SwinMR SwinMR Huang et al., MICCAI 2022 38.5 dB SSIM 0.921 Checkpoint unavailable	0.852	38.5	0.921	✓ Certified	Huang et al., MICCAI 2022
20	HybridCascade HybridCascade Fastmri, arXiv 2020 37.8 dB SSIM 0.917 Checkpoint unavailable	0.839	37.8	0.917	✓ Certified	Fastmri, arXiv 2020
21	MoDL MoDL Aggarwal et al., IEEE TMI 2019 36.5 dB SSIM 0.912 Checkpoint unavailable	0.814	36.5	0.912	✓ Certified	Aggarwal et al., IEEE TMI 2019
22	U-Net U-Net Zbontar et al., arXiv 2018 35.9 dB SSIM 0.904 Checkpoint unavailable	0.800	35.9	0.904	✓ Certified	Zbontar et al., arXiv 2018
23	DCCNN DCCNN Schlemper et al., IEEE TMI 2018 35.5 dB SSIM 0.908 Checkpoint unavailable	0.796	35.5	0.908	✓ Certified	Schlemper et al., IEEE TMI 2018
24	Deep-ADMM-Net Deep-ADMM-Net Yang et al., NeurIPS 2016 35.3 dB SSIM 0.907 Checkpoint unavailable	0.792	35.3	0.907	✓ Certified	Yang et al., NeurIPS 2016
25	PnP-DnCNN PnP-DnCNN Ahmad et al., IEEE TCI 2019 (DOI:10.1109/TCI.2019.2944521) 35.0 dB SSIM 0.905 Try in SpecLab →	0.786	35.0	0.905	✓ Certified	Ahmad et al., IEEE TCI 2019 (DOI:10.1109/TCI.2019.2944521)
26	ALOHA ALOHA Jin et al., IEEE TMI 2016 34.5 dB SSIM 0.900 Try in SpecLab →	0.775	34.5	0.900	✓ Certified	Jin et al., IEEE TMI 2016
27	BM3D-MRI BM3D-MRI Eksioglu, Comput. Math. Meth. Med. 2016 34.2 dB SSIM 0.897 Try in SpecLab →	0.769	34.2	0.897	✓ Certified	Eksioglu, Comput. Math. Meth. Med. 2016
28	LORAKS LORAKS Haldar, IEEE TMI 2014 33.8 dB SSIM 0.893 Try in SpecLab →	0.760	33.8	0.893	✓ Certified	Haldar, IEEE TMI 2014
29	ESPIRiT ESPIRiT Uecker et al., MRM 2014 33.4 dB SSIM 0.890 Try in SpecLab →	0.752	33.4	0.890	✓ Certified	Uecker et al., MRM 2014
30	k-t SPARSE-SENSE k-t SPARSE-SENSE Lustig et al., MRM 2006 32.5 dB SSIM 0.875 Try in SpecLab →	0.729	32.5	0.875	✓ Certified	Lustig et al., MRM 2006
31	L1-Wavelet L1-Wavelet Lustig et al., MRM 2007 32.1 dB SSIM 0.870 Try in SpecLab →	0.720	32.1	0.870	✓ Certified	Lustig et al., MRM 2007
32	Score-MRI Score-MRI Chung & Ye, Med. Image Anal. 2022 31.4 dB SSIM 0.890 Checkpoint unavailable	0.718	31.4	0.890	✓ Certified	Chung & Ye, Med. Image Anal. 2022
33	GRAPPA GRAPPA Griswold et al., MRM 2002 31.2 dB SSIM 0.860 Try in SpecLab →	0.700	31.2	0.860	✓ Certified	Griswold et al., MRM 2002
34	SENSE SENSE Pruessmann et al., MRM 1999 29.5 dB SSIM 0.830 Try in SpecLab →	0.657	29.5	0.830	✓ Certified	Pruessmann et al., MRM 1999
35	Zero-Filled IFFT Zero-Filled IFFT Pruessmann et al., MRM 1999 26.0 dB SSIM 0.620 Try in SpecLab →	0.493	26.0	0.620	✓ Certified	Pruessmann et al., MRM 1999

Dataset: PWM Benchmark (35 algorithms)

Blind Reconstruction Challenge — forward model has unknown mismatch, must calibrate from data. Score = 0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

#	Method	Overall Score	Public PSNR / SSIM	Dev PSNR / SSIM	Hidden PSNR / SSIM	Trust	Source
🥇	SwinMR++ + gradient SwinMR++ + gradient 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 Score 0.846 Correct & Reconstruct →	0.846	0.889 41.48 dB / 0.991	0.848 37.77 dB / 0.981	0.802 35.12 dB / 0.968	✓ 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
🥈	HUMUS-Net++ + gradient HUMUS-Net++ + gradient Fabian et al., dHUMUS-Net 2023 — k-space DC per module + dynamic multi-scale weighting + INR head + perceptual-structural loss + axial attention Score 0.845 Correct & Reconstruct →	0.845	0.880 40.63 dB / 0.989	0.842 38.96 dB / 0.985	0.812 36.88 dB / 0.978	✓ Certified	Fabian et al., dHUMUS-Net 2023 — k-space DC per module + dynamic multi-scale weighting + INR head + perceptual-structural loss + axial attention
🥉	ReconFormer++ + gradient ReconFormer++ + gradient Pan et al., IEEE TMI 2025 Score 0.837 Correct & Reconstruct →	0.837	0.882 40.12 dB / 0.988	0.823 36.29 dB / 0.975	0.805 35.96 dB / 0.973	✓ Certified	Pan et al., IEEE TMI 2025
4	HybridCascade++ + gradient HybridCascade++ + gradient HybridCascade++ MICCAI 2021 + IEEE TMI 2025 — multi-scale cascade DC + SIREN INR warm-start + SSIM structural anchor + DRUNet polish + freq-blend LF/HF fusion Score 0.835 Correct & Reconstruct →	0.835	0.873 40.02 dB / 0.988	0.828 35.85 dB / 0.973	0.805 34.53 dB / 0.965	✓ 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
5	PnP-DnCNN-Pro + gradient PnP-DnCNN-Pro + gradient 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 Score 0.822 Correct & Reconstruct →	0.822	0.878 39.54 dB / 0.987	0.797 34.06 dB / 0.961	0.790 34.11 dB / 0.962	✓ 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
6	PromptMR-SFM + gradient PromptMR-SFM + gradient PWM 2026 Score 0.820 Correct & Reconstruct →	0.820	0.880 39.73 dB / 0.987	0.807 35.01 dB / 0.968	0.772 32.8 dB / 0.951	✓ Certified	PWM 2026
7	U-Net++ + gradient U-Net++ + gradient 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 Score 0.811 Correct & Reconstruct →	0.811	0.864 39.51 dB / 0.987	0.789 34.34 dB / 0.963	0.779 33.43 dB / 0.956	✓ 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
8	MoDL-Net++ + gradient MoDL-Net++ + gradient 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 Score 0.804 Correct & Reconstruct →	0.804	0.888 40.79 dB / 0.990	0.791 33.89 dB / 0.960	0.734 30.82 dB / 0.929	✓ 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
9	MRI-FM + gradient MRI-FM + gradient Wang et al., Nature MI 2026 Score 0.804 Correct & Reconstruct →	0.804	0.869 39.25 dB / 0.986	0.796 33.69 dB / 0.958	0.747 30.91 dB / 0.930	✓ Certified	Wang et al., Nature MI 2026
10	MR-IPT + gradient MR-IPT + gradient Sci. Reports 2025 Score 0.800 Correct & Reconstruct →	0.800	0.892 40.84 dB / 0.990	0.774 31.94 dB / 0.942	0.733 29.5 dB / 0.909	✓ Certified	Sci. Reports 2025
11	PromptMR + gradient PromptMR + gradient Bai et al., ECCV 2024 Score 0.797 Correct & Reconstruct →	0.797	0.842 37.24 dB / 0.979	0.784 33.52 dB / 0.957	0.766 32.48 dB / 0.948	✓ Certified	Bai et al., ECCV 2024
12	E2E-VarNet + gradient E2E-VarNet + gradient Sriram et al., MICCAI 2020 Score 0.787 Correct & Reconstruct →	0.787	0.839 36.81 dB / 0.977	0.781 32.4 dB / 0.947	0.742 30.35 dB / 0.922	✓ Certified	Sriram et al., MICCAI 2020
13	SwinMR + gradient SwinMR + gradient Huang et al., MICCAI 2022 Score 0.786 Correct & Reconstruct →	0.786	0.848 37.07 dB / 0.978	0.788 33.55 dB / 0.957	0.722 30.22 dB / 0.920	✓ Certified	Huang et al., MICCAI 2022
14	BrainID-MRI + gradient BrainID-MRI + gradient Liu et al., CVPR 2025 Score 0.784 Correct & Reconstruct →	0.784	0.878 39.51 dB / 0.987	0.755 31.86 dB / 0.941	0.718 29.95 dB / 0.916	✓ Certified	Liu et al., CVPR 2025
15	HUMUS-Net + gradient HUMUS-Net + gradient Fabian et al., NeurIPS 2022 Score 0.775 Correct & Reconstruct →	0.775	0.833 36.45 dB / 0.976	0.769 31.59 dB / 0.938	0.723 29.52 dB / 0.909	✓ Certified	Fabian et al., NeurIPS 2022
16	ReconFormer + gradient ReconFormer + gradient Guo et al., IEEE TMI 2024 Score 0.769 Correct & Reconstruct →	0.769	0.833 36.15 dB / 0.974	0.758 31.23 dB / 0.934	0.716 29.31 dB / 0.906	✓ Certified	Guo et al., IEEE TMI 2024
17	MRDynamo + gradient MRDynamo + gradient Chen et al., NeurIPS 2024 Score 0.765 Correct & Reconstruct →	0.765	0.852 38.23 dB / 0.983	0.740 29.95 dB / 0.916	0.702 28.68 dB / 0.894	✓ Certified	Chen et al., NeurIPS 2024
18	MRI-DiffusionNet + gradient MRI-DiffusionNet + gradient Song et al., ICCV 2024 Score 0.763 Correct & Reconstruct →	0.763	0.849 37.98 dB / 0.982	0.741 30.79 dB / 0.928	0.700 27.88 dB / 0.878	✓ Certified	Song et al., ICCV 2024
19	MMR-Mamba + gradient MMR-Mamba + gradient Zhao et al., Med. Image Anal. 2025 Score 0.762 Correct & Reconstruct →	0.762	0.858 38.83 dB / 0.985	0.746 30.32 dB / 0.922	0.682 26.93 dB / 0.856	✓ Certified	Zhao et al., Med. Image Anal. 2025
20	MoDL + gradient MoDL + gradient Aggarwal et al., IEEE TMI 2019 Score 0.746 Correct & Reconstruct →	0.746	0.803 33.9 dB / 0.960	0.733 29.3 dB / 0.906	0.701 28.71 dB / 0.895	✓ Certified	Aggarwal et al., IEEE TMI 2019
21	BM3D-MRI + gradient BM3D-MRI + gradient Eksioglu, Comput. Math. Meth. Med. 2016 Score 0.742 Correct & Reconstruct →	0.742	0.794 32.66 dB / 0.949	0.734 30.46 dB / 0.924	0.699 27.9 dB / 0.879	✓ Certified	Eksioglu, Comput. Math. Meth. Med. 2016
22	PnP-DnCNN + gradient PnP-DnCNN + gradient Ahmad et al., IEEE TCI 2019 (DOI:10.1109/TCI.2019.2944521) Score 0.741 Correct & Reconstruct →	0.741	0.806 33.67 dB / 0.958	0.740 30.22 dB / 0.920	0.678 26.71 dB / 0.851	✓ Certified	Ahmad et al., IEEE TCI 2019 (DOI:10.1109/TCI.2019.2944521)
23	HybridCascade + gradient HybridCascade + gradient Fastmri, arXiv 2020 Score 0.739 Correct & Reconstruct →	0.739	0.818 35.1 dB / 0.968	0.728 29.12 dB / 0.902	0.670 26.27 dB / 0.839	✓ Certified	Fastmri, arXiv 2020
24	DCCNN + gradient DCCNN + gradient Schlemper et al., IEEE TMI 2018 Score 0.731 Correct & Reconstruct →	0.731	0.788 32.76 dB / 0.950	0.726 29.47 dB / 0.908	0.678 27.33 dB / 0.866	✓ Certified	Schlemper et al., IEEE TMI 2018
25	Deep-ADMM-Net + gradient Deep-ADMM-Net + gradient Yang et al., NeurIPS 2016 Score 0.706 Correct & Reconstruct →	0.706	0.808 33.69 dB / 0.958	0.678 26.55 dB / 0.847	0.632 25.41 dB / 0.815	✓ Certified	Yang et al., NeurIPS 2016
26	GRAPPA + gradient GRAPPA + gradient Griswold et al., MRM 2002 Score 0.702 Correct & Reconstruct →	0.702	0.720 28.44 dB / 0.890	0.707 28.69 dB / 0.895	0.679 26.66 dB / 0.850	✓ Certified	Griswold et al., MRM 2002
27	ALOHA + gradient ALOHA + gradient Jin et al., IEEE TMI 2016 Score 0.698 Correct & Reconstruct →	0.698	0.778 32.52 dB / 0.948	0.699 27.94 dB / 0.880	0.618 24.01 dB / 0.769	✓ Certified	Jin et al., IEEE TMI 2016
28	SENSE + gradient SENSE + gradient Pruessmann et al., MRM 1999 Score 0.680 Correct & Reconstruct →	0.680	0.725 28.5 dB / 0.891	0.688 27.32 dB / 0.866	0.626 25.11 dB / 0.806	✓ Certified	Pruessmann et al., MRM 1999
29	U-Net + gradient U-Net + gradient Zbontar et al., arXiv 2018 Score 0.670 Correct & Reconstruct →	0.670	0.817 34.52 dB / 0.965	0.644 24.71 dB / 0.793	0.549 21.54 dB / 0.670	✓ Certified	Zbontar et al., arXiv 2018
30	L1-Wavelet + gradient L1-Wavelet + gradient Lustig et al., MRM 2007 Score 0.652 Correct & Reconstruct →	0.652	0.765 30.65 dB / 0.926	0.623 24.58 dB / 0.789	0.568 22.46 dB / 0.709	✓ Certified	Lustig et al., MRM 2007
31	ESPIRiT + gradient ESPIRiT + gradient Uecker et al., MRM 2014 Score 0.639 Correct & Reconstruct →	0.639	0.782 31.8 dB / 0.940	0.600 23.24 dB / 0.740	0.534 20.84 dB / 0.638	✓ Certified	Uecker et al., MRM 2014
32	LORAKS + gradient LORAKS + gradient Haldar, IEEE TMI 2014 Score 0.634 Correct & Reconstruct →	0.634	0.767 31.61 dB / 0.938	0.614 23.54 dB / 0.752	0.521 21.17 dB / 0.653	✓ Certified	Haldar, IEEE TMI 2014
33	k-t SPARSE-SENSE + gradient k-t SPARSE-SENSE + gradient Lustig et al., MRM 2006 Score 0.632 Correct & Reconstruct →	0.632	0.743 29.78 dB / 0.913	0.613 24.05 dB / 0.770	0.539 21.6 dB / 0.673	✓ Certified	Lustig et al., MRM 2006
34	Score-MRI + gradient Score-MRI + gradient Chung & Ye, Med. Image Anal. 2022 Score 0.623 Correct & Reconstruct →	0.623	0.728 29.12 dB / 0.902	0.602 23.69 dB / 0.757	0.539 21.44 dB / 0.666	✓ Certified	Chung & Ye, Med. Image Anal. 2022
35	Zero-Filled IFFT + gradient Zero-Filled IFFT + gradient Pruessmann et al., MRM 1999 Score 0.574 Correct & Reconstruct →	0.574	0.624 24.2 dB / 0.776	0.568 22.52 dB / 0.712	0.531 20.67 dB / 0.630	✓ Certified	Pruessmann et al., MRM 1999

Complete score requires all 3 tiers (Public + Dev + Hidden).

Join the competition →

Scoring: 0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖) PSNR 40% · SSIM 40% · Consistency 20%

Public 3 scenes

Full-access development tier with all data visible.

What you get & how to use

What you get: Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.

How to use: Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.

What to submit: Reconstructed signals (x_hat) and corrected spec as HDF5.

Public Leaderboard

#	Method	Score	PSNR	SSIM
1	MR-IPT + gradient	0.892	40.84	0.99
2	SwinMR++ + gradient	0.889	41.48	0.991
3	MoDL-Net++ + gradient	0.888	40.79	0.99
4	ReconFormer++ + gradient	0.882	40.12	0.988
5	HUMUS-Net++ + gradient	0.880	40.63	0.989
6	PromptMR-SFM + gradient	0.880	39.73	0.987
7	PnP-DnCNN-Pro + gradient	0.878	39.54	0.987
8	BrainID-MRI + gradient	0.878	39.51	0.987
9	HybridCascade++ + gradient	0.873	40.02	0.988
10	MRI-FM + gradient	0.869	39.25	0.986
11	U-Net++ + gradient	0.864	39.51	0.987
12	MMR-Mamba + gradient	0.858	38.83	0.985
13	MRDynamo + gradient	0.852	38.23	0.983
14	MRI-DiffusionNet + gradient	0.849	37.98	0.982
15	SwinMR + gradient	0.848	37.07	0.978
16	PromptMR + gradient	0.842	37.24	0.979
17	E2E-VarNet + gradient	0.839	36.81	0.977
18	HUMUS-Net + gradient	0.833	36.45	0.976
19	ReconFormer + gradient	0.833	36.15	0.974
20	HybridCascade + gradient	0.818	35.1	0.968
21	U-Net + gradient	0.817	34.52	0.965
22	Deep-ADMM-Net + gradient	0.808	33.69	0.958
23	PnP-DnCNN + gradient	0.806	33.67	0.958
24	MoDL + gradient	0.803	33.9	0.96
25	BM3D-MRI + gradient	0.794	32.66	0.949
26	DCCNN + gradient	0.788	32.76	0.95
27	ESPIRiT + gradient	0.782	31.8	0.94
28	ALOHA + gradient	0.778	32.52	0.948
29	LORAKS + gradient	0.767	31.61	0.938
30	L1-Wavelet + gradient	0.765	30.65	0.926
31	k-t SPARSE-SENSE + gradient	0.743	29.78	0.913
32	Score-MRI + gradient	0.728	29.12	0.902
33	SENSE + gradient	0.725	28.5	0.891
34	GRAPPA + gradient	0.720	28.44	0.89
35	Zero-Filled IFFT + gradient	0.624	24.2	0.776

Spec Ranges (4 parameters)

Parameter	Min	Max	Unit
B0_inhomog	-2.0	4.0	ppm
head_motion	-1.0	2.0	mm
hemodynamic_delay	5.0	8.0	s
physiological_noise	-0.02	0.04

View Details →

Dev 3 scenes

Blind evaluation tier — no ground truth available.

What you get & how to use

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.

Dev Leaderboard

#	Method	Score	PSNR	SSIM
1	SwinMR++ + gradient	0.848	37.77	0.981
2	HUMUS-Net++ + gradient	0.842	38.96	0.985
3	HybridCascade++ + gradient	0.828	35.85	0.973
4	ReconFormer++ + gradient	0.823	36.29	0.975
5	PromptMR-SFM + gradient	0.807	35.01	0.968
6	PnP-DnCNN-Pro + gradient	0.797	34.06	0.961
7	MRI-FM + gradient	0.796	33.69	0.958
8	MoDL-Net++ + gradient	0.791	33.89	0.96
9	U-Net++ + gradient	0.789	34.34	0.963
10	SwinMR + gradient	0.788	33.55	0.957
11	PromptMR + gradient	0.784	33.52	0.957
12	E2E-VarNet + gradient	0.781	32.4	0.947
13	MR-IPT + gradient	0.774	31.94	0.942
14	HUMUS-Net + gradient	0.769	31.59	0.938
15	ReconFormer + gradient	0.758	31.23	0.934
16	BrainID-MRI + gradient	0.755	31.86	0.941
17	MMR-Mamba + gradient	0.746	30.32	0.922
18	MRI-DiffusionNet + gradient	0.741	30.79	0.928
19	MRDynamo + gradient	0.740	29.95	0.916
20	PnP-DnCNN + gradient	0.740	30.22	0.92
21	BM3D-MRI + gradient	0.734	30.46	0.924
22	MoDL + gradient	0.733	29.3	0.906
23	HybridCascade + gradient	0.728	29.12	0.902
24	DCCNN + gradient	0.726	29.47	0.908
25	GRAPPA + gradient	0.707	28.69	0.895
26	ALOHA + gradient	0.699	27.94	0.88
27	SENSE + gradient	0.688	27.32	0.866
28	Deep-ADMM-Net + gradient	0.678	26.55	0.847
29	U-Net + gradient	0.644	24.71	0.793
30	L1-Wavelet + gradient	0.623	24.58	0.789
31	LORAKS + gradient	0.614	23.54	0.752
32	k-t SPARSE-SENSE + gradient	0.613	24.05	0.77
33	Score-MRI + gradient	0.602	23.69	0.757
34	ESPIRiT + gradient	0.600	23.24	0.74
35	Zero-Filled IFFT + gradient	0.568	22.52	0.712

Spec Ranges (4 parameters)

Parameter	Min	Max	Unit
B0_inhomog	-2.4	3.6	ppm
head_motion	-1.2	1.8	mm
hemodynamic_delay	4.8	7.8	s
physiological_noise	-0.024	0.036

Submit Reconstruction View Details →

Hidden 3 scenes

Fully blind server-side evaluation — no data download.

What you get & how to use

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.

Hidden Leaderboard

#	Method	Score	PSNR	SSIM
1	HUMUS-Net++ + gradient	0.812	36.88	0.978
2	ReconFormer++ + gradient	0.805	35.96	0.973
3	HybridCascade++ + gradient	0.805	34.53	0.965
4	SwinMR++ + gradient	0.802	35.12	0.968
5	PnP-DnCNN-Pro + gradient	0.790	34.11	0.962
6	U-Net++ + gradient	0.779	33.43	0.956
7	PromptMR-SFM + gradient	0.772	32.8	0.951
8	PromptMR + gradient	0.766	32.48	0.948
9	MRI-FM + gradient	0.747	30.91	0.93
10	E2E-VarNet + gradient	0.742	30.35	0.922
11	MoDL-Net++ + gradient	0.734	30.82	0.929
12	MR-IPT + gradient	0.733	29.5	0.909
13	HUMUS-Net + gradient	0.723	29.52	0.909
14	SwinMR + gradient	0.722	30.22	0.92
15	BrainID-MRI + gradient	0.718	29.95	0.916
16	ReconFormer + gradient	0.716	29.31	0.906
17	MRDynamo + gradient	0.702	28.68	0.894
18	MoDL + gradient	0.701	28.71	0.895
19	MRI-DiffusionNet + gradient	0.700	27.88	0.878
20	BM3D-MRI + gradient	0.699	27.9	0.879
21	MMR-Mamba + gradient	0.682	26.93	0.856
22	GRAPPA + gradient	0.679	26.66	0.85
23	PnP-DnCNN + gradient	0.678	26.71	0.851
24	DCCNN + gradient	0.678	27.33	0.866
25	HybridCascade + gradient	0.670	26.27	0.839
26	Deep-ADMM-Net + gradient	0.632	25.41	0.815
27	SENSE + gradient	0.626	25.11	0.806
28	ALOHA + gradient	0.618	24.01	0.769
29	L1-Wavelet + gradient	0.568	22.46	0.709
30	U-Net + gradient	0.549	21.54	0.67
31	k-t SPARSE-SENSE + gradient	0.539	21.6	0.673
32	Score-MRI + gradient	0.539	21.44	0.666
33	ESPIRiT + gradient	0.534	20.84	0.638
34	Zero-Filled IFFT + gradient	0.531	20.67	0.63
35	LORAKS + gradient	0.521	21.17	0.653

Spec Ranges (4 parameters)

Parameter	Min	Max	Unit
B0_inhomog	-1.4	4.6	ppm
head_motion	-0.7	2.3	mm
hemodynamic_delay	5.3	8.3	s
physiological_noise	-0.014	0.046

Submit Algorithm View Details →

Blind Reconstruction Challenge

Challenge

Given measurements with unknown mismatch and spec ranges (not exact params), reconstruct the original signal. A method must be evaluated on all three tiers for a complete score. Scored on a composite metric: 0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖).

Input

Measurements y, ideal forward model H, spec ranges

Output

Reconstructed signal x̂

About the Imaging Modality

Functional MRI detects neural activity indirectly via the blood-oxygen-level dependent (BOLD) contrast mechanism. Active brain regions increase local blood flow and oxygenation, altering the ratio of diamagnetic oxyhemoglobin to paramagnetic deoxyhemoglobin, causing T2* signal changes of 1-5%. Data is acquired with fast gradient-echo EPI sequences at high temporal resolution (TR 0.5-2s). The forward model includes the hemodynamic response function (HRF) convolved with neural activity. Primary challenges include physiological noise, head motion, and the low CNR of the BOLD signal.

Principle

Functional MRI detects brain activity indirectly through the Blood Oxygen Level Dependent (BOLD) contrast mechanism. Neural activity increases local blood flow and oxygenation, changing the ratio of diamagnetic oxyhemoglobin to paramagnetic deoxyhemoglobin. This alters the local T2* relaxation time, producing a small (~1-5 %) signal change detectable by gradient-echo EPI sequences acquired rapidly at whole-brain coverage.

How to Build the System

Use a 3T MRI scanner with a 32-64 channel head coil. Acquire multi-band (simultaneous multi-slice) gradient-echo EPI sequences (TR 0.5-1.5 s, TE ~30 ms, 2 mm isotropic voxels, multiband factor 4-8). Include a high-resolution T1w structural scan for registration. Physiological monitoring (pulse oximetry, respiratory bellows) enables noise regression. Use foam padding to minimize head motion.

Common Reconstruction Algorithms

General Linear Model (GLM) for task-based fMRI (FSL FEAT, SPM)
ICA (Independent Component Analysis) for resting-state networks
Seed-based functional connectivity analysis
Motion correction and nuisance regression (6-parameter rigid body + CompCor)
Deep-learning denoising and parcellation (BrainNetCNN, fMRIPrep pipeline)

Common Mistakes

Excessive head motion causing false activations or connectivity artifacts
Not correcting for physiological noise (cardiac, respiratory) in the signal
Insufficient statistical correction for multiple comparisons (inflated false positives)
Using too long a TR, missing the hemodynamic response in fast event-related designs
Geometric distortion in EPI not corrected before registration to structural scan

How to Avoid Mistakes

Use prospective motion correction and strict motion exclusion criteria (<0.5 mm FD)
Acquire and regress physiological signals; use ICA-based denoising (ICA-AROMA)
Apply proper multiple-comparison correction (FWE, FDR, cluster-based thresholding)
Use multiband EPI for sub-second TR to adequately sample the HRF
Acquire field maps (B₀) and apply distortion correction (topup, fieldmap-based)

Forward-Model Mismatch Cases

The widefield fallback applies spatial Gaussian blur, but fMRI measures the BOLD (Blood Oxygen Level Dependent) signal via T2*-weighted MRI — the hemodynamic response function (HRF) convolution with neural activity is completely absent
fMRI acquisition occurs in k-space (Fourier domain) with EPI readout, and the signal of interest is a tiny (~1-5%) temporal modulation — the widefield spatial blur cannot model the temporal hemodynamic dynamics or k-space encoding

How to Correct the Mismatch

Use the fMRI operator that models BOLD signal generation: y(t) = FFT_acquisition(x_baseline * (1 + delta_BOLD(t))), where delta_BOLD = HRF * neural_activity encodes brain activation
Analyze using GLM (general linear model) with the hemodynamic response function, or ICA/connectivity analysis, applied to correctly modeled time-series MRI data

Experimental Setup — Signal Chain

Experimental setup diagram for Functional MRI (BOLD)

Reconstruction Gallery — 4 Scenes × 3 Scenarios

Method: CPU_baseline | Mismatch: nominal (nominal=True, perturbed=False)

I Ideal II Mismatch (uncorrected) III Oracle correction

I — Ideal

Ground Truth

Measurement

Reconstruction

16.584200964385797 dB SSIM 0.3250905580130585

II — Mismatch

Ground Truth

Measurement

Reconstruction

12.506523882319042 dB SSIM 0.044716474341606735

III — Oracle

Ground Truth

Measurement (perturbed)

Reconstruction

15.931719256055723 dB SSIM 0.12142227244746187

I — Ideal

Ground Truth

Measurement

Reconstruction

18.884299556167566 dB SSIM 0.3252411352628261

II — Mismatch

Ground Truth

Measurement

Reconstruction

13.742128073130111 dB SSIM 0.048029248758291385

III — Oracle

Ground Truth

Measurement (perturbed)

Reconstruction

17.02391116326154 dB SSIM 0.11622117666622035

I — Ideal

Ground Truth

Measurement

Reconstruction

18.301830801494646 dB SSIM 0.32976160086547746

II — Mismatch

Ground Truth

Measurement

Reconstruction

13.36792873190369 dB SSIM 0.04964364838717723

III — Oracle

Ground Truth

Measurement (perturbed)

Reconstruction

16.550610792224443 dB SSIM 0.1259030967011571

I — Ideal

Ground Truth

Measurement

Reconstruction

16.67775192548055 dB SSIM 0.3247822274360334

II — Mismatch

Ground Truth

Measurement

Reconstruction

12.53195005733068 dB SSIM 0.044966709218701925

III — Oracle

Ground Truth

Measurement (perturbed)

Reconstruction

15.850507045662987 dB SSIM 0.12621817875162467

Mean PSNR Across All Scenes

17.61202081188214 dB

I — Ideal

13.037132686170882 dB

II — Mismatch

16.33918706430117 dB

III — Oracle

Degradation: -4.6 dB Recovery: +3.3 dB

Per-scene PSNR breakdown (4 scenes)

Scene	I (PSNR)	I (SSIM)	II (PSNR)	II (SSIM)	III (PSNR)	III (SSIM)
scene_00	16.584200964385797	0.3250905580130585	12.506523882319042	0.044716474341606735	15.931719256055723	0.12142227244746187
scene_01	18.884299556167566	0.3252411352628261	13.742128073130111	0.048029248758291385	17.02391116326154	0.11622117666622035
scene_02	18.301830801494646	0.32976160086547746	13.36792873190369	0.04964364838717723	16.550610792224443	0.1259030967011571
scene_03	16.67775192548055	0.3247822274360334	12.53195005733068	0.044966709218701925	15.850507045662987	0.12621817875162467
Mean	17.61202081188214	0.3262188803943489	13.037132686170882	0.046839020176444326	16.33918706430117	0.122441181141616

Experimental Setup

Instrument: Siemens MAGNETOM Prisma (HCP protocol)

Field Strength T: 3.0

Voxel Size Mm: 2x2x2

Tr S: 0.72

Te Ms: 33.1

Matrix Size: 104x90

Slices: 72

Multiband Factor: 8

Sequence: gradient-echo EPI

Paradigm: resting-state / task-based

Dataset: HCP 3T (1200 subjects)

Key References

Ogawa et al., 'Brain magnetic resonance imaging with contrast dependent on blood oxygenation', PNAS 87, 9868-9872 (1990)
Glasser et al., 'The minimal preprocessing pipelines for the Human Connectome Project', NeuroImage 80, 105-124 (2013)

Canonical Datasets

Human Connectome Project (HCP) 3T (1200 subjects)
UK Biobank brain imaging

Spec DAG — Forward Model Pipeline

F(EPI) → Σ_t → D(g, η₁)

F EPI k-Space Readout (EPI)

Σ Temporal Averaging (t)

D RF Coil Receiver (g, η₁)

Mismatch Parameters

Symbol	Parameter	Description	Nominal	Perturbed
ΔB₀	B0_inhomog	B₀ field inhomogeneity (ppm)	0	2.0
Δr	head_motion	Head motion (mm)	0	1.0
Δτ	hemodynamic_delay	HRF delay error (s)	6.0	7.0
σ_p	physiological_noise	Physiological noise amplitude	0	0.02

Credits System

40%

Platform Profit Pool

Revenue allocated to benchmark rewards

30%

Winner Share

Top algorithm receives from pool

$100

Min Withdrawal

Minimum payout threshold

Spec Primitives Reference (11 primitives)

P Propagation

Free-space or medium propagation kernel (Fresnel, Rayleigh-Sommerfeld).

M Mask / Modulation

Spatial or spatio-temporal amplitude modulation (coded aperture, SLM pattern).

Π Projection

Geometric projection operator (Radon transform, fan-beam, cone-beam).

F Fourier Sampling

Sampling in the Fourier / k-space domain (MRI, ptychography).

C Convolution

Shift-invariant convolution with a point-spread function (PSF).

Σ Summation / Integration

Summation along a physical dimension (spectral, temporal, angular).

D Detector

Sensor readout with gain g and noise model η (Gaussian, Poisson, mixed).

S Structured Illumination

Patterned illumination (block, Hadamard, random) applied to the scene.

W Wavelength Dispersion

Spectral dispersion element (prism, grating) with shift α and aperture a.

R Rotation / Motion

Sample or gantry rotation (CT, electron tomography).

Λ Wavelength Selection

Spectral filter or monochromator selecting a wavelength band.