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
Edge-weighted pFISTA-Net for MRI Reconstruction—0
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions—0
Efficient Structurally-Strengthened Generative Adversarial Network for MRI Reconstruction—0
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI—0
Encoding Semantic Priors into the Weights of Implicit Neural Representation—0
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI—0
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction—0
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI—0
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction—0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions—0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review—0
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction—0
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI—0
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction—0
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction—0
GA-HQS: MRI reconstruction via a generically accelerated unfolding approach—0
Generalising Deep Learning MRI Reconstruction across Different Domains—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
GRAPPA-GANs for Parallel MRI Reconstruction—0
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction—0
Greedy Learning for Large-Scale Neural MRI Reconstruction—0
Guided MRI Reconstruction via Schrödinger Bridge—0
Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty—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