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

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Showing 251–300 of 441 papers

TitleStatusHype
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations—0
MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion—0
Model-based free-breathing cardiac MRI reconstruction using deep learned \& STORM priors: MoDL-STORM—0
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 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
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging—0
Physics-driven Deep Learning for PET/MRI—0
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence—0
pISTA-SENSE-ResNet for Parallel MRI Reconstruction—0
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification—0
Predicting 4D Liver MRI for MR-guided Interventions—0
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery—0
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction—0
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization—0
Pyramid Convolutional RNN for MRI Image Reconstruction—0
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data—0
Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction—0
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder—0
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery—0
Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer—0
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding—0
Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation—0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction—0
Risk Quantification in Deep MRI Reconstruction—0
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction—0
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction—0
Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model—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