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 276–300 of 441 papers

TitleStatusHype
Predicting 4D Liver MRI for MR-guided Interventions—0
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion ModelsCode1
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction—0
ReconFormer: Accelerated MRI Reconstruction Using Recurrent TransformerCode1
Fast MRI Reconstruction: How Powerful Transformers Are?Code1
Swin Transformer for Fast MRICode1
Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstructionCode0
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI—0
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information—0
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
Contrastive Learning for Local and Global Learning MRI Reconstruction—0
Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer—0
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI ReconstructionCode1
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI—0
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight PredictionCode0
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI ReconstructionCode1
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction—0
Greedy Learning for Large-Scale Neural MRI Reconstruction—0
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple ContrastsCode0
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric—0
MRI Recovery with A Self-calibrated Denoiser—0
Complex-valued Federated Learning with Differential Privacy and MRI Applications—0
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement—0
Accelerated First Order Methods for Variational ImagingCode0
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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