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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 41–50 of 441 papers

Title	Date	Tasks	Status	Hype
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and Reconstruction	Jan 1, 2023	MRI ReconstructionSuper-Resolution	CodeCode Available	1
A Neural-Network-Based Convex Regularizer for Inverse Problems	Nov 22, 2022	DenoisingImage Reconstruction	CodeCode Available	1
T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction	Sep 8, 2022	Image ReconstructionMRI Reconstruction	CodeCode Available	1
High-Frequency Space Diffusion Models for Accelerated MRI	Aug 10, 2022	DenoisingImage Generation	CodeCode Available	1
Adaptive Diffusion Priors for Accelerated MRI Reconstruction	Jul 12, 2022	De-aliasingMRI Reconstruction	CodeCode Available	1
K-Space Transformer for Undersampled MRI Reconstruction	Jun 14, 2022	DecoderInductive Bias	CodeCode Available	1
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm Regularizations	Jun 2, 2022	Computational EfficiencyMRI Reconstruction	CodeCode Available	1
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise	May 20, 2022	DenoisingImage Reconstruction	CodeCode Available	1
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction	Apr 21, 2022	MRI Reconstruction	CodeCode Available	1
Monarch: Expressive Structured Matrices for Efficient and Accurate Training	Apr 1, 2022	Language ModelingLanguage Modelling	CodeCode Available	1

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All datasets fastMRI Knee 8x fastMRI Knee Val 8x fastMRI Brain 4x fastMRI Brain 8x fastMRI Knee 4x IXI

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	HUMUS-Net (train+val data)	SSIM	0.89	—	Unverified
2	HUMUS-Net (train only)	SSIM	0.89	—	Unverified
3	End-to-end variational network	SSIM	0.89	—	Unverified
4	XPDNet	SSIM	0.89	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PromptMR	SSIM	0.9	—	Unverified
2	HUMUS-Net-L	SSIM	0.9	—	Unverified
3	HUMUS-Net	SSIM	0.89	—	Unverified
4	E2E-VarNet (train+val)	SSIM	0.89	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	End-to-end variational network	SSIM	0.96	—	Unverified
2	XPDNet	SSIM	0.96	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	End-to-end variational network	SSIM	0.94	—	Unverified
2	XPDNet	SSIM	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	End-to-end variational network	SSIM	0.93	—	Unverified
2	XPDNet	SSIM	0.93	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Residual U-NET	DSSIM	0	—	Unverified