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 26–50 of 441 papers

TitleStatusHype
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation—0
How Should We Evaluate Uncertainty in Accelerated MRI Reconstruction?—0
A Comprehensive Survey on Magnetic Resonance Image Reconstruction—0
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach—0
Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations—0
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shiftingCode1
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction—0
Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty—0
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
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing IntegrationCode2
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
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
A Trust-Guided Approach to MR Image Reconstruction with Side InformationCode0
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
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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