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 101–150 of 441 papers

TitleStatusHype
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI—0
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks—0
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction—0
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction—0
ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction—0
Deep Cardiac MRI Reconstruction with ADMM—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review—0
DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
Data augmentation for deep learning based accelerated MRI reconstruction—0
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation—0
A Densely Interconnected Network for Deep Learning Accelerated MRI—0
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers—0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies—0
A Scale Invariant Approach for Sparse Signal Recovery—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
A review and experimental evaluation of deep learning methods for MRI reconstruction—0
Deep Learning-based Intraoperative MRI Reconstruction—0
A Comprehensive Survey on Magnetic Resonance Image Reconstruction—0
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review—0
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis—0
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction—0
Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach—0
Cross-Modal Vertical Federated Learning for MRI Reconstruction—0
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction—0
A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects—0
Covariance-Free Sparse Bayesian Learning—0
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction—0
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning—0
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction—0
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling—0
Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals—0
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning—0
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems—0
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network—0
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction—0
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI—0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions—0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction—0
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings—0
Contrastive Learning for Local and Global Learning MRI Reconstruction—0
Encoding Semantic Priors into the Weights of Implicit Neural Representation—0
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction—0
A Deep Error Correction Network for Compressed Sensing MRI—0
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI—0
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
Conditional WGANs with Adaptive Gradient Balancing for Sparse 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