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 326–350 of 441 papers

TitleStatusHype
Undersampled MRI Reconstruction with Side Information-Guided Normalisation—0
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
Universal Undersampled MRI Reconstruction—0
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching—0
Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction—0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
Uncertainty Quantification in Deep MRI Reconstruction—0
X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models—0
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space—0
IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction—0
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI—0
Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network—0
Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction—0
Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model—0
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model—0
Joint Supervised and Self-supervised Learning for MRI Reconstruction—0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Uncertainty Estimation and Propagation in Accelerated MRI ReconstructionCode0
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative PriorsCode0
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank ApproximationCode0
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRICode0
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI ReconstructionCode0
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR ImagesCode0
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRICode0
A Deep Learning Approach Using Masked Image Modeling for Reconstruction of Undersampled K-spacesCode0
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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