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

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Showing 151–200 of 441 papers

TitleStatusHype
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple ContrastsCode0
MRI Reconstruction Using Deep Energy-Based ModelCode0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI ReconstructionCode0
MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolationCode0
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight PredictionCode0
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank ApproximationCode0
MambaRecon: MRI Reconstruction with Structured State Space ModelsCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
MDPG: Multi-domain Diffusion Prior Guidance for MRI ReconstructionCode0
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI ReconstructionCode0
LMO: Linear Mamba Operator for MRI ReconstructionCode0
CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI ReconstructionCode0
K-space and Image Domain Collaborative Energy based Model for Parallel MRI ReconstructionCode0
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRICode0
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil ConfigurationsCode0
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space SubsetsCode0
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image TranslationCode0
Learning Deep MRI Reconstruction Models from Scratch in Low-Data RegimesCode0
Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation DiscretizationCode0
Robust Physics-based Deep MRI Reconstruction Via Diffusion PurificationCode0
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic LossCode0
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI ReconstructionCode0
Erase to Enhance: Data-Efficient Machine Unlearning in MRI ReconstructionCode0
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
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Fast Controllable Diffusion Models for Undersampled MRI ReconstructionCode0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI ReconstructionCode0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative PriorsCode0
High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRICode0
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion ModelsCode0
An Unsupervised Deep Learning Method for Multi-coil Cine MRICode0
AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI ReconstructionCode0
Deep Parallel MRI Reconstruction Network Without Coil SensitivitiesCode0
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural RepresentationCode0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
GLEAM: Greedy Learning for Large-Scale Accelerated MRI ReconstructionCode0
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow EstimationCode0
Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference DataCode0
Self-supervised feature learning for cardiac Cine MR image reconstructionCode0
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks—0
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction—0
Deep learning for undersampled MRI reconstruction—0
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review—0
Deep Learning-based Intraoperative MRI Reconstruction—0
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study—0
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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