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 401–425 of 441 papers

TitleStatusHype
Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI ReconstructionCode0
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset—0
Efficient Structurally-Strengthened Generative Adversarial Network for MRI Reconstruction—0
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks—0
VS-Net: Variable splitting network for accelerated parallel MRI reconstructionCode0
Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging—0
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network—0
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction—0
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space—0
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction—0
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks—0
Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation—0
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding—0
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction—0
Reducing Uncertainty in Undersampled MRI Reconstruction with Active AcquisitionCode0
Generalising Deep Learning MRI Reconstruction across Different Domains—0
Uncertainty Quantification in Deep MRI Reconstruction—0
A Scale Invariant Approach for Sparse Signal Recovery—0
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction—0
MRI Reconstruction via Cascaded Channel-wise Attention NetworkCode1
Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imagingCode0
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)—0
Model-based free-breathing cardiac MRI reconstruction using deep learned \& STORM priors: MoDL-STORM—0
Adversarial and Perceptual Refinement for Compressed Sensing MRI ReconstructionCode0
Coupled Dictionary Learning for Multi-contrast 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