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

Showing 6170 of 441 papers

TitleStatusHype
Guided MRI Reconstruction via Schrödinger Bridge0
Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging ProblemsCode0
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction0
An All-in-one Approach for Accelerated Cardiac MRI Reconstruction0
On the Foundation Model for Cardiac MRI Reconstruction0
A Learned Proximal Alternating Minimization Algorithm and Its Induced Network for a Class of Two-block Nonconvex and Nonsmooth Optimization0
Benchmarking 3D multi-coil NC-PDNet MRI reconstruction0
Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI ReconstructionCode0
Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion ModelCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HUMUS-Net (train+val data)SSIM0.89Unverified
2HUMUS-Net (train only)SSIM0.89Unverified
3End-to-end variational networkSSIM0.89Unverified
4XPDNetSSIM0.89Unverified
#ModelMetricClaimedVerifiedStatus
1PromptMRSSIM0.9Unverified
2HUMUS-Net-LSSIM0.9Unverified
3HUMUS-NetSSIM0.89Unverified
4E2E-VarNet (train+val)SSIM0.89Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.96Unverified
2XPDNetSSIM0.96Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.94Unverified
2XPDNetSSIM0.94Unverified
#ModelMetricClaimedVerifiedStatus
1End-to-end variational networkSSIM0.93Unverified
2XPDNetSSIM0.93Unverified
#ModelMetricClaimedVerifiedStatus
1Residual U-NETDSSIM0Unverified