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 151–175 of 441 papers

TitleStatusHype
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction—0
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction—0
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction—0
Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction—0
GA-HQS: MRI reconstruction via a generically accelerated unfolding approach—0
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance—0
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI—0
Addressing The False Negative Problem of MRI Reconstruction Networks by Adversarial Attacks and Robust Training—0
Generalising Deep Learning MRI Reconstruction across Different Domains—0
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI—0
Complex-valued Federated Learning with Differential Privacy and MRI Applications—0
Efficient Structurally-Strengthened Generative Adversarial Network for MRI Reconstruction—0
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions—0
An All-in-one Approach for Accelerated Cardiac MRI Reconstruction—0
Edge-weighted pFISTA-Net for MRI Reconstruction—0
Encoding Semantic Priors into the Weights of Implicit Neural Representation—0
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information—0
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction—0
Coil Sketching for computationally-efficient MR iterative reconstruction—0
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
A New k-Space Model for Non-Cartesian Fourier Imaging—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
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network—0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—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