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 126–150 of 441 papers

TitleStatusHype
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization—0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies—0
Deep Learning-based Intraoperative MRI Reconstruction—0
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI ReconstructionCode0
Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems—0
Deep Unfolding Network with Spatial Alignment for multi-modal MRI reconstruction—0
Deep Learning-based MRI Reconstruction with Artificial Fourier Transform Network (AFTNet)Code0
Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency RegularizationCode0
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)Code0
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction—0
Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction—0
Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network—0
SubZero: Subspace Zero-Shot MRI ReconstructionCode0
Joint Supervised and Self-supervised Learning for MRI Reconstruction—0
Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumorsCode0
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRICode0
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRICode1
Fast Controllable Diffusion Models for Undersampled MRI ReconstructionCode0
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR ImagesCode0
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction—0
k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image ReconstructionCode1
Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction—0
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model—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