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 51–100 of 441 papers

TitleStatusHype
High-Frequency Space Diffusion Models for Accelerated MRICode1
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructionCode1
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRICode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance ImagingCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
Joint Frequency and Image Space Learning for MRI Reconstruction and AnalysisCode1
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction AlgorithmsCode1
Is good old GRAPPA dead?Code1
Adversarial Robust Training of Deep Learning MRI Reconstruction ModelsCode1
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRICode1
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised LearningCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
Benchmarking MRI Reconstruction Neural Networks on Large Public DatasetsCode1
Adaptive Diffusion Priors for Accelerated MRI ReconstructionCode1
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating OptimizationCode1
k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image ReconstructionCode1
Deep MRI Reconstruction with Radial SubsamplingCode1
Deep Low-rank plus Sparse Network for Dynamic MR ImagingCode1
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image ReconstructionCode1
Accelerated MRI with Un-trained Neural NetworksCode1
Density Compensated Unrolled Networks for Non-Cartesian MRI ReconstructionCode1
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion ModelsCode1
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion ModelsCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 PriorCode1
Task Transformer Network for Joint MRI Reconstruction and Super-ResolutionCode1
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse ProblemsCode1
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative ModelsCode1
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data GenerationCode1
Fast MRI Reconstruction via Edge AttentionCode1
Fast MRI Reconstruction: How Powerful Transformers Are?Code1
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
Regularization-Agnostic Compressed Sensing MRI Reconstruction with HypernetworksCode1
ContextMRI: Enhancing Compressed Sensing MRI through Metadata ConditioningCode1
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2NoiseCode1
Self-Supervised MRI Reconstruction with Unrolled Diffusion ModelsCode1
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical EvaluationCode1
An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior DistillationCode0
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine ImagingCode0
Adversarial and Perceptual Refinement for Compressed Sensing MRI ReconstructionCode0
A Trust-Guided Approach to MR Image Reconstruction with Side InformationCode0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Accelerated First Order Methods for Variational ImagingCode0
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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