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 91–100 of 441 papers

Title	Date	Tasks	Status	Hype	Score
ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning	Jan 8, 2025	Anatomycompressed sensing	CodeCode Available	1	5
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise	May 20, 2022	DenoisingImage Reconstruction	CodeCode Available	1	5
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models	Jun 29, 2023	MRI Reconstruction	CodeCode Available	1	5
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation	Mar 14, 2022	Image ReconstructionMRI Reconstruction	CodeCode Available	1	5
An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillation	May 14, 2022	compressed sensingDeep Learning	CodeCode Available	0	5
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging	Jul 3, 2024	Image ReconstructionMRI Reconstruction	CodeCode Available	0	5
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction	Jun 28, 2018	compressed sensingMRI Reconstruction	CodeCode Available	0	5
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRI	Feb 17, 2025	MRI Reconstruction	CodeCode Available	0	5
A Trust-Guided Approach to MR Image Reconstruction with Side Information	Jan 6, 2025	DiagnosticImage Reconstruction	CodeCode Available	0	5
Accelerated First Order Methods for Variational Imaging	Oct 6, 2021	DenoisingMRI Reconstruction	CodeCode Available	0	5

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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