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 176–200 of 441 papers

TitleStatusHype
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase AugmentationCode1
Learning Fourier-Constrained Diffusion Bridges for MRI ReconstructionCode1
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI ReconstructionCode0
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image ModelingCode1
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning HypernetworksCode0
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank ApproximationCode0
Self-Supervised MRI Reconstruction with Unrolled Diffusion ModelsCode1
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
Attention Hybrid Variational Net for Accelerated MRI Reconstruction—0
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI—0
Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRICode0
CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI ReconstructionCode0
PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI ReconstructionCode0
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative ModelsCode1
Cross-Modal Vertical Federated Learning for MRI Reconstruction—0
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI ReconstructionCode0
Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism Spectrum Disorder Through Convolutional Neural Network-Based Analysis of Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced Autism Diagnosis—0
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI ReconstructionCode0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI ReconstructionCode1
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI researchCode1
Coil Sketching for computationally-efficient MR iterative reconstruction—0
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction—0
DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction—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