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 176–200 of 441 papers

TitleStatusHype
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
MRI Reconstruction with Side Information using Diffusion Models—0
Edge-weighted pFISTA-Net for MRI Reconstruction—0
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information—0
Coil Sketching for computationally-efficient MR iterative reconstruction—0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT^2LR-Net)—0
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction—0
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI—0
Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction—0
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction—0
A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction—0
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization—0
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP—0
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction—0
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network—0
GA-HQS: MRI reconstruction via a generically accelerated unfolding approach—0
Generalising Deep Learning MRI Reconstruction across Different Domains—0
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction—0
Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction—0
DuDoUniNeXt: Dual-domain unified hybrid model for single and multi-contrast undersampled MRI reconstruction—0
Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field—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