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 251–275 of 441 papers

TitleStatusHype
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings—0
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning—0
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization—0
Accelerated MRI With Deep Linear Convolutional Transform Learning—0
Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations—0
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning—0
Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach—0
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction—0
A Comprehensive Survey on Magnetic Resonance Image Reconstruction—0
Active Deep Probabilistic Subsampling—0
Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction—0
Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction—0
Addressing The False Negative Problem of MRI Reconstruction Networks by Adversarial Attacks and Robust Training—0
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction—0
A Deep Error Correction Network for Compressed Sensing MRI—0
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction—0
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis—0
A Densely Interconnected Network for Deep Learning Accelerated MRI—0
ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction—0
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging—0
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors—0
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis—0
A Learned Proximal Alternating Minimization Algorithm and Its Induced Network for a Class of Two-block Nonconvex and Nonsmooth Optimization—0
ALMA: a mathematics-driven approach for determining tuning parameters in generalized LASSO problems, with applications to MRI—0
A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction—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