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 376–400 of 441 papers

TitleStatusHype
MambaRecon: MRI Reconstruction with Structured State Space ModelsCode0
Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion ModelCode0
MDPG: Multi-domain Diffusion Prior Guidance for MRI ReconstructionCode0
Fast Controllable Diffusion Models for Undersampled MRI ReconstructionCode0
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural RepresentationCode0
Synthetic PET via Domain Translation of 3D MRICode0
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial NetworksCode0
An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior DistillationCode0
Erase to Enhance: Data-Efficient Machine Unlearning in MRI ReconstructionCode0
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)Code0
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow EstimationCode0
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine ImagingCode0
A Trust-Guided Approach to MR Image Reconstruction with Side InformationCode0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI ReconstructionCode0
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
MRI Reconstruction Using Deep Energy-Based ModelCode0
Robust Physics-based Deep MRI Reconstruction Via Diffusion PurificationCode0
Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging ProblemsCode0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic LossCode0
Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imagingCode0
MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled DataCode0
Deep Parallel MRI Reconstruction Network Without Coil SensitivitiesCode0
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