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 251–275 of 441 papers

TitleStatusHype
MoRe-3DGSMR: Motion-resolved reconstruction framework for free-breathing pulmonary MRI based on 3D Gaussian representation—0
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding—0
Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework—0
MRI Image Reconstruction via Learning Optimization Using Neural ODEs—0
MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)—0
MRI Recovery with A Self-calibrated Denoiser—0
MR Optimized Reconstruction of Simultaneous Multi-Slice Imaging Using Diffusion Model—0
Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation—0
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction—0
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI—0
Multi-scale MRI reconstruction via dilated ensemble networks—0
NeRF Solves Undersampled MRI Reconstruction—0
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)—0
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models—0
ODE-based Deep Network for MRI Reconstruction—0
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations—0
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction—0
On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction—0
On the Empirical Effect of Gaussian Noise in Under-sampled MRI Reconstruction—0
On the Foundation Model for Cardiac MRI Reconstruction—0
On the Robustness of deep learning-based MRI Reconstruction to image transformations—0
Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems—0
Over-and-Under Complete Convolutional RNN for MRI Reconstruction—0
Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI—0
Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac 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