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 351–375 of 441 papers

TitleStatusHype
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image TranslationCode0
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors—0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Universal Undersampled MRI Reconstruction—0
Memory-efficient Learning for High-Dimensional MRI Reconstruction—0
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations—0
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction—0
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
GRAPPA-GANs for Parallel MRI Reconstruction—0
Data augmentation for deep learning based accelerated MRI reconstruction—0
Active Deep Probabilistic Subsampling—0
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine—0
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction—0
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery—0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil ConfigurationsCode0
Risk Quantification in Deep MRI Reconstruction—0
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding—0
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI—0
Deep Parallel MRI Reconstruction Network Without Coil SensitivitiesCode0
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction—0
MRI Image Reconstruction via Learning Optimization Using Neural ODEs—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