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 226–250 of 441 papers

TitleStatusHype
Deep unfolding as iterative regularization for imaging inverse problems—0
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
On the Robustness of deep learning-based MRI Reconstruction to image transformations—0
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance—0
Stable Deep MRI Reconstruction using Generative Priors—0
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging—0
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence—0
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation—0
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDUCode0
A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction—0
T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstructionCode1
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction—0
MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolationCode0
A Deep Learning Approach Using Masked Image Modeling for Reconstruction of Undersampled K-spacesCode0
High-Frequency Space Diffusion Models for Accelerated MRICode1
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled DataCode0
GLEAM: Greedy Learning for Large-Scale Accelerated MRI ReconstructionCode0
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction—0
Adaptive Diffusion Priors for Accelerated MRI ReconstructionCode1
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction—0
A Densely Interconnected Network for Deep Learning Accelerated MRI—0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions—0
A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects—0
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction—0
K-Space Transformer for Undersampled MRI ReconstructionCode1
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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