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 351–400 of 441 papers

TitleStatusHype
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
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors—0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Universal Undersampled MRI Reconstruction—0
Memory-efficient Learning for High-Dimensional MRI Reconstruction—0
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations—0
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
GRAPPA-GANs for Parallel MRI Reconstruction—0
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
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction—0
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery—0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil ConfigurationsCode0
Risk Quantification in Deep MRI Reconstruction—0
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding—0
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI—0
Deep Parallel MRI Reconstruction Network Without Coil SensitivitiesCode0
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction—0
MRI Image Reconstruction via Learning Optimization Using Neural ODEs—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks—0
Deep variational network for rapid 4D flow MRI reconstruction—0
High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRICode0
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction—0
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction—0
Learning to Predict Error for MRI Reconstruction—0
Addressing The False Negative Problem of MRI Reconstruction Networks by Adversarial Attacks and Robust Training—0
Breaking Speed Limits with Simultaneous Ultra-Fast MRI Reconstruction and Tissue Segmentation—0
Spherical function regularization for parallel MRI reconstruction—0
ODE-based Deep Network for MRI Reconstruction—0
An Unsupervised Deep Learning Method for Multi-coil Cine MRICode0
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms—0
Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference DataCode0
Pyramid Convolutional RNN for MRI Image Reconstruction—0
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI ReconstructionCode0
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)—0
Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation DiscretizationCode0
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction—0
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled DataCode0
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial NetworksCode0
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings—0
Time-Dependent Deep Image Prior for Dynamic MRICode0
pISTA-SENSE-ResNet for Parallel MRI Reconstruction—0
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network—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