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 351–375 of 441 papers

TitleStatusHype
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image TranslationCode0
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space SubsetsCode0
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion ModelsCode0
K-space and Image Domain Collaborative Energy based Model for Parallel MRI ReconstructionCode0
High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRICode0
GLEAM: Greedy Learning for Large-Scale Accelerated MRI ReconstructionCode0
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning HypernetworksCode0
Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI ReconstructionCode0
Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI ReconstructionCode0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
Learning Deep MRI Reconstruction Models from Scratch in Low-Data RegimesCode0
VS-Net: Variable splitting network for accelerated parallel MRI reconstructionCode0
Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal EquivarianceCode0
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI ReconstructionCode0
Reducing Uncertainty in Undersampled MRI Reconstruction with Active AcquisitionCode0
LMO: Linear Mamba Operator for MRI ReconstructionCode0
Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI ReconstructionCode0
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI ReconstructionCode0
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic resultsCode0
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight PredictionCode0
Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image ReconstructionCode0
SubZero: Subspace Zero-Shot 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