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 51–75 of 441 papers

TitleStatusHype
Fast MRI Reconstruction via Edge AttentionCode1
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion ModelsCode1
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse ProblemsCode1
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI DataCode1
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
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data GenerationCode1
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRICode1
Adversarial Robust Training of Deep Learning MRI Reconstruction ModelsCode1
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRICode1
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image ModelingCode1
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructionCode1
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?Code1
Benchmarking MRI Reconstruction Neural Networks on Large Public DatasetsCode1
Adaptive Diffusion Priors for Accelerated MRI ReconstructionCode1
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI ReconstructionCode1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching LossCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRICode1
Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI ReconstructionCode1
Accelerated MRI with Un-trained Neural NetworksCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
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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