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 126–150 of 441 papers

TitleStatusHype
Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach—0
Cross-Modal Vertical Federated Learning for MRI Reconstruction—0
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction—0
A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects—0
Covariance-Free Sparse Bayesian Learning—0
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction—0
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning—0
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI—0
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling—0
Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals—0
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning—0
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems—0
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network—0
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction—0
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction—0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions—0
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings—0
Contrastive Learning for Local and Global Learning MRI Reconstruction—0
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI—0
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction—0
A Deep Error Correction Network for Compressed Sensing MRI—0
Encoding Semantic Priors into the Weights of Implicit Neural Representation—0
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
Conditional WGANs with Adaptive Gradient Balancing for Sparse 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