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 1–25 of 441 papers

TitleStatusHype
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-ProblemsCode2
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023Code2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing IntegrationCode2
Density Compensated Unrolled Networks for Non-Cartesian MRI ReconstructionCode1
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion ModelsCode1
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 PriorCode1
Accelerated MRI with Un-trained Neural NetworksCode1
Data augmentation for deep learning based accelerated MRI reconstruction with limited dataCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
Deep Low-rank plus Sparse Network for Dynamic MR ImagingCode1
Deep MRI Reconstruction with Radial SubsamplingCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance ImagingCode1
Adaptive Diffusion Priors for Accelerated MRI ReconstructionCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
Benchmarking MRI Reconstruction Neural Networks on Large Public DatasetsCode1
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating OptimizationCode1
Adversarial Robust Training of Deep Learning MRI Reconstruction ModelsCode1
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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