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
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructionCode1
Deep Low-rank plus Sparse Network for Dynamic MR ImagingCode1
Density Compensated Unrolled Networks for Non-Cartesian MRI ReconstructionCode1
Learning Multiscale Convolutional Dictionaries for Image ReconstructionCode1
Adversarial Robust Training of Deep Learning MRI Reconstruction ModelsCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
Adaptive Diffusion Priors for 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
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRICode1
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 PriorCode1
Benchmarking MRI Reconstruction Neural Networks on Large Public DatasetsCode1
End-to-End Variational Networks for Accelerated MRI ReconstructionCode1
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
Accelerated MRI with Un-trained Neural NetworksCode1
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion ModelsCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase AugmentationCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating OptimizationCode1
Data augmentation for deep learning based accelerated MRI reconstruction with limited dataCode1
Fast MRI Reconstruction: How Powerful Transformers Are?Code1
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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