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 76–100 of 441 papers

TitleStatusHype
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion ModelsCode1
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion ModelsCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 PriorCode1
Task Transformer Network for Joint MRI Reconstruction and Super-ResolutionCode1
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse ProblemsCode1
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative ModelsCode1
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data GenerationCode1
Fast MRI Reconstruction via Edge AttentionCode1
Fast MRI Reconstruction: How Powerful Transformers Are?Code1
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
Regularization-Agnostic Compressed Sensing MRI Reconstruction with HypernetworksCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2NoiseCode1
Self-Supervised MRI Reconstruction with Unrolled Diffusion ModelsCode1
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical EvaluationCode1
An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior DistillationCode0
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine ImagingCode0
Adversarial and Perceptual Refinement for Compressed Sensing MRI ReconstructionCode0
A Trust-Guided Approach to MR Image Reconstruction with Side InformationCode0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Accelerated First Order Methods for Variational ImagingCode0
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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