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 301–350 of 441 papers

TitleStatusHype
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised LearningCode1
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training FrameworkCode1
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction—0
An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil SensitivitiesCode0
A review and experimental evaluation of deep learning methods for MRI reconstruction—0
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic resultsCode0
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data—0
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI DataCode1
MRI Reconstruction Using Deep Energy-Based ModelCode0
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching LossCode1
Deep MRI Reconstruction with Radial SubsamplingCode1
Multi-Modal MRI Reconstruction Assisted with Spatial Alignment NetworkCode1
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization—0
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss—0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction—0
Uncertainty-Guided Progressive GANs for Medical Image TranslationCode1
Data augmentation for deep learning based accelerated MRI reconstruction with limited dataCode1
Over-and-Under Complete Convolutional RNN for MRI Reconstruction—0
Task Transformer Network for Joint MRI Reconstruction and Super-ResolutionCode1
Is good old GRAPPA dead?Code1
Sparse recovery based on the generalized error function—0
Covariance-Free Sparse Bayesian Learning—0
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI—0
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning—0
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI ReconstructionCode0
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial TransformersCode1
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image TranslationCode0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors—0
Universal Undersampled MRI Reconstruction—0
Memory-efficient Learning for High-Dimensional MRI Reconstruction—0
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating OptimizationCode1
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations—0
Zero-Shot Self-Supervised Learning for MRI ReconstructionCode1
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction—0
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Regularization-Agnostic Compressed Sensing MRI Reconstruction with HypernetworksCode1
GRAPPA-GANs for Parallel MRI Reconstruction—0
Density Compensated Unrolled Networks for Non-Cartesian MRI ReconstructionCode1
Data augmentation for deep learning based accelerated MRI reconstruction—0
Active Deep Probabilistic Subsampling—0
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine—0
Results of the 2020 fastMRI Challenge for Machine Learning MR Image ReconstructionCode1
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction—0
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery—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