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 51–75 of 441 papers

TitleStatusHype
End-to-End Variational Networks for Accelerated MRI ReconstructionCode1
Fast MRI Reconstruction: How Powerful Transformers Are?Code1
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI DataCode1
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse ProblemsCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance ImagingCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised LearningCode1
Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI ReconstructionCode1
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRICode1
Adversarial Robust Training of Deep Learning MRI Reconstruction ModelsCode1
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRICode1
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase AugmentationCode1
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
Benchmarking MRI Reconstruction Neural Networks on Large Public DatasetsCode1
Adaptive Diffusion Priors for Accelerated MRI ReconstructionCode1
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image ModelingCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRICode1
Accelerated MRI with Un-trained Neural NetworksCode1
Joint Frequency and Image Space Learning for MRI Reconstruction and AnalysisCode1
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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