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
Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction—0
INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstruction—0
Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals—0
Encoding Semantic Priors into the Weights of Implicit Neural Representation—0
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning—0
Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework—0
Erase to Enhance: Data-Efficient Machine Unlearning in MRI ReconstructionCode0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—0
Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI—0
Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction—0
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction—0
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models—0
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review—0
Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach—0
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty EstimationCode0
IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction—0
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI—0
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRICode0
DuDoUniNeXt: Dual-domain unified hybrid model for single and multi-contrast undersampled MRI reconstruction—0
Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image ReconstructionCode0
NeRF Solves Undersampled MRI Reconstruction—0
Diffusion Posterior Sampling is Computationally Intractable—0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR 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