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 376–400 of 441 papers

TitleStatusHype
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