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 326–350 of 441 papers

TitleStatusHype
Greedy Learning for Large-Scale Neural MRI Reconstruction—0
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple ContrastsCode0
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric—0
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction—0
MRI Recovery with A Self-calibrated Denoiser—0
Complex-valued Federated Learning with Differential Privacy and MRI Applications—0
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement—0
Accelerated First Order Methods for Variational ImagingCode0
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction—0
An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil SensitivitiesCode0
A review and experimental evaluation of deep learning methods for MRI reconstruction—0
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic resultsCode0
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data—0
MRI Reconstruction Using Deep Energy-Based ModelCode0
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization—0
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss—0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction—0
Over-and-Under Complete Convolutional RNN for MRI Reconstruction—0
Sparse recovery based on the generalized error function—0
Covariance-Free Sparse Bayesian Learning—0
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI—0
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning—0
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel 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