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 251–300 of 441 papers

TitleStatusHype
Physics-driven Deep Learning for PET/MRI—0
Synthetic PET via Domain Translation of 3D MRICode0
Invertible Sharpening Network for MRI Reconstruction Enhancement—0
Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT^2LR-Net)—0
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm RegularizationsCode1
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2NoiseCode1
An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior DistillationCode0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis—0
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI ReconstructionCode1
Accelerated MRI With Deep Linear Convolutional Transform Learning—0
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis—0
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers—0
Monarch: Expressive Structured Matrices for Efficient and Accurate TrainingCode1
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction—0
A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction—0
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging—0
K-space and Image Domain Collaborative Energy based Model for Parallel MRI ReconstructionCode0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructionCode1
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical EvaluationCode1
Undersampled MRI Reconstruction with Side Information-Guided Normalisation—0
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image ReconstructionCode1
Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI ReconstructionCode0
Predicting 4D Liver MRI for MR-guided Interventions—0
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion ModelsCode1
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction—0
ReconFormer: Accelerated MRI Reconstruction Using Recurrent TransformerCode1
Fast MRI Reconstruction: How Powerful Transformers Are?Code1
Swin Transformer for Fast MRICode1
Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstructionCode0
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI—0
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information—0
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
Contrastive Learning for Local and Global Learning MRI Reconstruction—0
Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer—0
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI ReconstructionCode1
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI—0
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight PredictionCode0
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI ReconstructionCode1
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction—0
Greedy Learning for Large-Scale Neural MRI Reconstruction—0
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple ContrastsCode0
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric—0
MRI Recovery with A Self-calibrated Denoiser—0
Complex-valued Federated Learning with Differential Privacy and MRI Applications—0
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement—0
Accelerated First Order Methods for Variational ImagingCode0
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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