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 176–200 of 441 papers

TitleStatusHype
Robust Physics-based Deep MRI Reconstruction Via Diffusion PurificationCode0
Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging ProblemsCode0
Fast Controllable Diffusion Models for Undersampled MRI ReconstructionCode0
Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRICode0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI ReconstructionCode0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRICode0
An Unsupervised Deep Learning Method for Multi-coil Cine MRICode0
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI ReconstructionCode0
An unsupervised method for MRI recovery: Deep image prior with structured sparsityCode0
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative PriorsCode0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI ReconstructionCode0
Deep Parallel MRI Reconstruction Network Without Coil SensitivitiesCode0
Segmentation-guided MRI reconstruction for meaningfully diverse reconstructionsCode0
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural RepresentationCode0
GLEAM: Greedy Learning for Large-Scale Accelerated MRI ReconstructionCode0
Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference DataCode0
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion ModelsCode0
Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imagingCode0
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks—0
Deep Learning Methods for Parallel Magnetic Resonance 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