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 411–420 of 441 papers

Title	Date	Tasks	Status
Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors	Nov 22, 2023	AnatomyDiagnostic	CodeCode Available
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI	Mar 8, 2024	DenoisingImage Reconstruction	CodeCode Available
Time-Dependent Deep Image Prior for Dynamic MRI	Oct 3, 2019	MRI Reconstruction	CodeCode Available
Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation Discretization	Nov 26, 2019	Image ReconstructionMRI Reconstruction	CodeCode Available
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps	Sep 27, 2023	Image ReconstructionMRI Reconstruction	CodeCode Available
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimation	Apr 6, 2024	MRI ReconstructionSSIM	CodeCode Available
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data	Aug 8, 2022	Decision MakingImage Reconstruction	CodeCode Available
Self-supervised feature learning for cardiac Cine MR image reconstruction	May 29, 2025	Image ReconstructionMRI Reconstruction	CodeCode Available
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction	Jul 25, 2023	Image ReconstructionMedical Diagnosis	CodeCode Available
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement	Sep 23, 2023	Image Super-ResolutionMRI Reconstruction	CodeCode Available

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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