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 326–350 of 441 papers

TitleStatusHype
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning—0
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI ReconstructionCode0
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial TransformersCode1
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
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors—0
Universal Undersampled MRI Reconstruction—0
Memory-efficient Learning for High-Dimensional MRI Reconstruction—0
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating OptimizationCode1
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations—0
Zero-Shot Self-Supervised Learning for MRI ReconstructionCode1
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
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Regularization-Agnostic Compressed Sensing MRI Reconstruction with HypernetworksCode1
GRAPPA-GANs for Parallel MRI Reconstruction—0
Density Compensated Unrolled Networks for Non-Cartesian MRI ReconstructionCode1
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
Results of the 2020 fastMRI Challenge for Machine Learning MR Image ReconstructionCode1
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction—0
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery—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