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 201–250 of 441 papers

TitleStatusHype
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding—0
Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation—0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction—0
Risk Quantification in Deep MRI Reconstruction—0
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction—0
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction—0
Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model—0
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging—0
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction—0
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction—0
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction—0
Learning to Predict Error for MRI Reconstruction—0
Smooth optimization algorithms for global and locally low-rank regularizers—0
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation—0
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields—0
Sparse recovery based on the generalized error function—0
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction—0
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction—0
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction—0
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI—0
Spherical function regularization for parallel MRI reconstruction—0
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI—0
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging—0
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric—0
Stable Deep MRI Reconstruction using Generative Priors—0
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures—0
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction—0
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning—0
Three-Dimensional MRI Reconstruction with Gaussian Representations: Tackling the Undersampling Problem—0
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks—0
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement—0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Undersampled MRI Reconstruction with Side Information-Guided Normalisation—0
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
Universal Undersampled MRI Reconstruction—0
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching—0
Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction—0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
Uncertainty Quantification in Deep MRI Reconstruction—0
X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models—0
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space—0
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction—0
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings—0
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning—0
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization—0
Accelerated MRI With Deep Linear Convolutional Transform Learning—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