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
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching—0
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation—0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRICode0
Three-Dimensional MRI Reconstruction with Gaussian Representations: Tackling the Undersampling Problem—0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
Domain-conditioned and Temporal-guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction—0
Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction—0
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction—0
A Trust-Guided Approach to MR Image Reconstruction with Side InformationCode0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
Training-Free Mitigation of Adversarial Attacks on Deep Learning-Based MRI Reconstruction—0
An unsupervised method for MRI recovery: Deep image prior with structured sparsityCode0
LMO: Linear Mamba Operator for MRI ReconstructionCode0
AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI ReconstructionCode0
MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)—0
Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation—0
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification—0
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction—0
Guided MRI Reconstruction via Schrödinger Bridge—0
Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging ProblemsCode0
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
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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