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
Physics-driven Deep Learning for PET/MRI—0
Synthetic PET via Domain Translation of 3D MRICode0
Invertible Sharpening Network for MRI Reconstruction Enhancement—0
Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT^2LR-Net)—0
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm RegularizationsCode1
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2NoiseCode1
An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior DistillationCode0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis—0
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI ReconstructionCode1
Accelerated MRI With Deep Linear Convolutional Transform Learning—0
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis—0
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers—0
Monarch: Expressive Structured Matrices for Efficient and Accurate TrainingCode1
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction—0
A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction—0
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging—0
K-space and Image Domain Collaborative Energy based Model for Parallel MRI ReconstructionCode0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructionCode1
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical EvaluationCode1
Undersampled MRI Reconstruction with Side Information-Guided Normalisation—0
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image ReconstructionCode1
Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI ReconstructionCode0
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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