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

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Showing 1–10 of 441 papers

TitleStatusHype
MDPG: Multi-domain Diffusion Prior Guidance for MRI ReconstructionCode0
Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction—0
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction—0
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction—0
Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction—0
Implicit Neural Representation-Based MRI Reconstruction Method with Sensitivity Map Constraints—0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction—0
Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction—0
Self-supervised feature learning for cardiac Cine MR image reconstructionCode0
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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