SOTAVerified

MRI Reconstruction

In its most basic form, MRI reconstruction consists in retrieving a complex-valued image from its under-sampled Fourier coefficients. Besides, it can be addressed as a encoder-decoder task, in which the normative model in the latent space will only capture the relevant information without noise or corruptions. Then, we decode the latent space in order to have a reconstructed MRI.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 441 papers

TitleStatusHype
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder—0
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine—0
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery—0
Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer—0
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding—0
Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation—0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction—0
Risk Quantification in Deep MRI Reconstruction—0
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction—0
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction—0
Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model—0
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging—0
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction—0
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction—0
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction—0
Learning to Predict Error for MRI Reconstruction—0
Smooth optimization algorithms for global and locally low-rank regularizers—0
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation—0
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields—0
Sparse recovery based on the generalized error function—0
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction—0
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction—0
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction—0
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All datasetsfastMRI Knee 8xfastMRI Knee Val 8xfastMRI Brain 4xfastMRI Brain 8xfastMRI Knee 4xIXI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HUMUS-Net (train+val data)SSIM0.89—Unverified
2HUMUS-Net (train only)SSIM0.89—Unverified
3End-to-end variational networkSSIM0.89—Unverified
4XPDNetSSIM0.89—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptMRSSIM0.9—Unverified
2HUMUS-Net-LSSIM0.9—Unverified
3HUMUS-NetSSIM0.89—Unverified
4E2E-VarNet (train+val)SSIM0.89—Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.96—Unverified
2XPDNetSSIM0.96—Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.94—Unverified
2XPDNetSSIM0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.93—Unverified
2XPDNetSSIM0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1Residual U-NETDSSIM0—Unverified