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
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging—0
Physics-driven Deep Learning for PET/MRI—0
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence—0
pISTA-SENSE-ResNet for Parallel MRI Reconstruction—0
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification—0
Predicting 4D Liver MRI for MR-guided Interventions—0
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery—0
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction—0
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization—0
Pyramid Convolutional RNN for MRI Image Reconstruction—0
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data—0
Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction—0
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
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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