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

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Showing 76–100 of 441 papers

TitleStatusHype
Regularization-Agnostic Compressed Sensing MRI Reconstruction with HypernetworksCode1
Density Compensated Unrolled Networks for Non-Cartesian MRI ReconstructionCode1
Results of the 2020 fastMRI Challenge for Machine Learning MR Image ReconstructionCode1
Learning Multiscale Convolutional Dictionaries for Image ReconstructionCode1
Adversarial Robust Training of Deep Learning MRI Reconstruction ModelsCode1
Deep Low-rank plus Sparse Network for Dynamic MR ImagingCode1
XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challengeCode1
Unsupervised MRI Reconstruction with Generative Adversarial NetworksCode1
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
Accelerated MRI with Un-trained Neural NetworksCode1
Joint Frequency and Image Space Learning for MRI Reconstruction and AnalysisCode1
End-to-End Variational Networks for Accelerated MRI ReconstructionCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement LearningCode1
Benchmarking MRI Reconstruction Neural Networks on Large Public DatasetsCode1
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 PriorCode1
MRI Reconstruction Using Deep Bayesian EstimationCode1
MRI Reconstruction via Cascaded Channel-wise Attention NetworkCode1
MDPG: Multi-domain Diffusion Prior Guidance for MRI ReconstructionCode0
Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction—0
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction—0
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction—0
Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction—0
Implicit Neural Representation-Based MRI Reconstruction Method with Sensitivity Map Constraints—0
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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