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 76–100 of 441 papers

TitleStatusHype
Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal EquivarianceCode0
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model—0
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual LearningCode1
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling—0
MambaRecon: MRI Reconstruction with Structured State Space ModelsCode0
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature ScalingCode1
Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model—0
MR Optimized Reconstruction of Simultaneous Multi-Slice Imaging Using Diffusion Model—0
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative PriorsCode0
Segmentation-guided MRI reconstruction for meaningfully diverse reconstructionsCode0
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine ImagingCode0
IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural RepresentationsCode1
MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion—0
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRICode0
ALMA: a mathematics-driven approach for determining tuning parameters in generalized LASSO problems, with applications to MRI—0
Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction—0
INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstruction—0
Encoding Semantic Priors into the Weights of Implicit Neural Representation—0
Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals—0
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning—0
Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework—0
Erase to Enhance: Data-Efficient Machine Unlearning in MRI ReconstructionCode0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—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