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 101–150 of 441 papers

TitleStatusHype
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRICode1
Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI—0
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion ModelsCode1
Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction—0
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction—0
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance ImagingCode1
X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models—0
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review—0
Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach—0
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty EstimationCode0
IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction—0
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023Code2
Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI ReconstructionCode1
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRICode1
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI—0
DuDoUniNeXt: Dual-domain unified hybrid model for single and multi-contrast undersampled MRI reconstruction—0
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRICode0
Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image ReconstructionCode0
Diffusion Posterior Sampling is Computationally Intractable—0
NeRF Solves Undersampled MRI Reconstruction—0
TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet methodCode1
MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion modelCode1
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction—0
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization—0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies—0
Deep Learning-based Intraoperative MRI Reconstruction—0
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI ReconstructionCode0
Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems—0
Deep Unfolding Network with Spatial Alignment for multi-modal MRI reconstruction—0
Deep Learning-based MRI Reconstruction with Artificial Fourier Transform Network (AFTNet)Code0
Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency RegularizationCode0
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)Code0
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction—0
Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction—0
Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network—0
SubZero: Subspace Zero-Shot MRI ReconstructionCode0
Joint Supervised and Self-supervised Learning for MRI Reconstruction—0
Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumorsCode0
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRICode0
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRICode1
Fast Controllable Diffusion Models for Undersampled MRI ReconstructionCode0
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR ImagesCode0
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction—0
k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image ReconstructionCode1
Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction—0
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model—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