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 151–175 of 441 papers

TitleStatusHype
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight PredictionCode0
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRICode0
K-space and Image Domain Collaborative Energy based Model for Parallel MRI ReconstructionCode0
Learning Deep MRI Reconstruction Models from Scratch in Low-Data RegimesCode0
MambaRecon: MRI Reconstruction with Structured State Space ModelsCode0
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity mapsCode0
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank ApproximationCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI ReconstructionCode0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRICode0
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion ModelsCode0
CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI ReconstructionCode0
High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRICode0
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image TranslationCode0
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI ReconstructionCode0
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI ReconstructionCode0
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space SubsetsCode0
Robust Physics-based Deep MRI Reconstruction Via Diffusion PurificationCode0
Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging ProblemsCode0
Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRICode0
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic LossCode0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
Erase to Enhance: Data-Efficient Machine Unlearning in MRI ReconstructionCode0
Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal EquivarianceCode0
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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