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
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructionCode1
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion ModelCode1
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
Data augmentation for deep learning based accelerated MRI reconstruction with limited dataCode1
K-Space Transformer for Undersampled MRI ReconstructionCode1
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction AlgorithmsCode1
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI researchCode1
Monarch: Expressive Structured Matrices for Efficient and Accurate TrainingCode1
MRI Reconstruction Using Deep Bayesian EstimationCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shiftingCode1
Multi-Modal MRI Reconstruction Assisted with Spatial Alignment NetworkCode1
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks—0
A Comprehensive Survey on Magnetic Resonance Image Reconstruction—0
Attention Hybrid Variational Net for Accelerated MRI Reconstruction—0
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction—0
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks—0
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction—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