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 276–300 of 441 papers

TitleStatusHype
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction—0
An All-in-one Approach for Accelerated Cardiac MRI Reconstruction—0
A New k-Space Model for Non-Cartesian Fourier Imaging—0
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network—0
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling—0
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction—0
A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects—0
A review and experimental evaluation of deep learning methods for MRI reconstruction—0
A Scale Invariant Approach for Sparse Signal Recovery—0
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks—0
Attention Hybrid Variational Net for Accelerated MRI Reconstruction—0
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction—0
Benchmarking 3D multi-coil NC-PDNet MRI reconstruction—0
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction—0
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification—0
Breaking Speed Limits with Simultaneous Ultra-Fast MRI Reconstruction and Tissue Segmentation—0
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)—0
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation—0
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI—0
Coil Sketching for computationally-efficient MR iterative reconstruction—0
Complex-valued Federated Learning with Differential Privacy and MRI Applications—0
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance—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