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 301–325 of 441 papers

TitleStatusHype
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network—0
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction—0
MRI Reconstruction with Side Information using Diffusion Models—0
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction—0
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction—0
Contrastive Learning for Local and Global Learning MRI Reconstruction—0
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems—0
Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals—0
Covariance-Free Sparse Bayesian Learning—0
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction—0
Cross-Modal Vertical Federated Learning for MRI Reconstruction—0
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies—0
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers—0
Data augmentation for deep learning based accelerated MRI reconstruction—0
DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Deep Cardiac MRI Reconstruction with ADMM—0
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction—0
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study—0
Deep Learning-based Intraoperative MRI Reconstruction—0
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review—0
Deep learning for undersampled MRI reconstruction—0
Deep Learning Methods for Parallel Magnetic Resonance Image 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