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
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction—0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
Self-supervised feature learning for cardiac Cine MR image reconstructionCode0
Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction—0
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction—0
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models—0
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations—0
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion ModelsCode0
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference—0
Smooth optimization algorithms for global and locally low-rank regularizers—0
A New k-Space Model for Non-Cartesian Fourier Imaging—0
MoRe-3DGSMR: Motion-resolved reconstruction framework for free-breathing pulmonary MRI based on 3D Gaussian representation—0
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization—0
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI ReconstructionCode0
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions—0
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction—0
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation—0
How Should We Evaluate Uncertainty in Accelerated MRI Reconstruction?—0
A Comprehensive Survey on Magnetic Resonance Image Reconstruction—0
Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations—0
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach—0
Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty—0
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction—0
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching—0
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation—0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRICode0
Three-Dimensional MRI Reconstruction with Gaussian Representations: Tackling the Undersampling Problem—0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
Domain-conditioned and Temporal-guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction—0
Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction—0
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction—0
A Trust-Guided Approach to MR Image Reconstruction with Side InformationCode0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
Training-Free Mitigation of Adversarial Attacks on Deep Learning-Based MRI Reconstruction—0
An unsupervised method for MRI recovery: Deep image prior with structured sparsityCode0
LMO: Linear Mamba Operator for MRI ReconstructionCode0
AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI ReconstructionCode0
MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)—0
Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation—0
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification—0
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction—0
Guided MRI Reconstruction via Schrödinger Bridge—0
Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging ProblemsCode0
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
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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