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 401–425 of 441 papers

TitleStatusHype
MS-Glance: Bio-Insipred Non-semantic Context Vectors and their Applications in Supervising Image ReconstructionCode0
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil ConfigurationsCode0
An unsupervised method for MRI recovery: Deep image prior with structured sparsityCode0
Accelerated First Order Methods for Variational ImagingCode0
An Unsupervised Deep Learning Method for Multi-coil Cine MRICode0
Deep Learning-based MRI Reconstruction with Artificial Fourier Transform Network (AFTNet)Code0
AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI ReconstructionCode0
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple ContrastsCode0
Segmentation-guided MRI reconstruction for meaningfully diverse reconstructionsCode0
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI ReconstructionCode0
Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumorsCode0
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRICode0
Time-Dependent Deep Image Prior for Dynamic MRICode0
Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation DiscretizationCode0
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity mapsCode0
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty EstimationCode0
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled DataCode0
Self-supervised feature learning for cardiac Cine MR image reconstructionCode0
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI ReconstructionCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference DataCode0
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI ReconstructionCode0
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled DataCode0
Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency RegularizationCode0
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDUCode0
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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