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 251–275 of 441 papers

TitleStatusHype
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI ReconstructionCode0
Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism Spectrum Disorder Through Convolutional Neural Network-Based Analysis of Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced Autism Diagnosis—0
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI ReconstructionCode0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Coil Sketching for computationally-efficient MR iterative reconstruction—0
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction—0
DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction—0
MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled DataCode0
Uncertainty-Aware Null Space Networks for Data-Consistent Image ReconstructionCode0
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI—0
GA-HQS: MRI reconstruction via a generically accelerated unfolding approach—0
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study—0
MRI Reconstruction with Side Information using Diffusion Models—0
Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field—0
Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review—0
SMUG: Towards robust MRI reconstruction by smoothed unrollingCode0
Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion—0
Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and SynthesisCode0
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification—0
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction—0
Edge-weighted pFISTA-Net for MRI Reconstruction—0
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP—0
On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction—0
Learning Deep MRI Reconstruction Models from Scratch in Low-Data RegimesCode0
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI 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