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 1–50 of 441 papers

TitleStatusHype
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing IntegrationCode2
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023Code2
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-ProblemsCode2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRICode1
Is good old GRAPPA dead?Code1
Joint Frequency and Image Space Learning for MRI Reconstruction and AnalysisCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm RegularizationsCode1
Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI ReconstructionCode1
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching LossCode1
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructionCode1
Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI ReconstructionCode1
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion ModelCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
Deep MRI Reconstruction with Radial SubsamplingCode1
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
Deep Low-rank plus Sparse Network for Dynamic MR ImagingCode1
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 PriorCode1
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI DataCode1
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image ModelingCode1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
High-Frequency Space Diffusion Models for Accelerated MRICode1
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural RepresentationsCode1
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRICode1
Learning Multiscale Convolutional Dictionaries for Image ReconstructionCode1
Adversarial Robust Training of Deep Learning MRI Reconstruction ModelsCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
Benchmarking MRI Reconstruction Neural Networks on Large Public DatasetsCode1
Adaptive Diffusion Priors for Accelerated MRI ReconstructionCode1
Accelerated MRI with Un-trained Neural NetworksCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
Data augmentation for deep learning based accelerated MRI reconstruction with limited dataCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating OptimizationCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion ModelsCode1
End-to-End Variational Networks for Accelerated MRI ReconstructionCode1
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse ProblemsCode1
Fast MRI Reconstruction: How Powerful Transformers Are?Code1
Fast MRI Reconstruction via Edge AttentionCode1
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance ImagingCode1
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase AugmentationCode1
Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI ReconstructionCode1
Density Compensated Unrolled Networks for Non-Cartesian MRI ReconstructionCode1
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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