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 151–175 of 441 papers

TitleStatusHype
Deep Cardiac MRI Reconstruction with ADMM—0
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging—0
Multi-scale MRI reconstruction via dilated ensemble networks—0
SMRD: SURE-based Robust MRI Reconstruction with Diffusion ModelsCode1
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity mapsCode0
Unveiling Fairness Biases in Deep Learning-Based Brain MRI ReconstructionCode0
Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI ReconstructionCode1
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer—0
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-ProblemsCode2
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems—0
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction—0
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural RepresentationCode0
Robust Physics-based Deep MRI Reconstruction Via Diffusion PurificationCode0
Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI ReconstructionCode0
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction—0
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion ModelCode1
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction AlgorithmsCode1
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction—0
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse ProblemsCode0
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning—0
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI ReconstructionCode0
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space SubsetsCode0
Uncertainty Estimation and Propagation in Accelerated MRI ReconstructionCode0
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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