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 301–325 of 441 papers

TitleStatusHype
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised LearningCode1
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training FrameworkCode1
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction—0
An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil SensitivitiesCode0
A review and experimental evaluation of deep learning methods for MRI reconstruction—0
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic resultsCode0
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data—0
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI DataCode1
MRI Reconstruction Using Deep Energy-Based ModelCode0
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching LossCode1
Deep MRI Reconstruction with Radial SubsamplingCode1
Multi-Modal MRI Reconstruction Assisted with Spatial Alignment NetworkCode1
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization—0
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss—0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction—0
Uncertainty-Guided Progressive GANs for Medical Image TranslationCode1
Data augmentation for deep learning based accelerated MRI reconstruction with limited dataCode1
Over-and-Under Complete Convolutional RNN for MRI Reconstruction—0
Task Transformer Network for Joint MRI Reconstruction and Super-ResolutionCode1
Is good old GRAPPA dead?Code1
Sparse recovery based on the generalized error function—0
Covariance-Free Sparse Bayesian Learning—0
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI—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