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	Score
A Neural-Network-Based Convex Regularizer for Inverse Problems	Nov 22, 2022	DenoisingImage Reconstruction	CodeCode Available	1	5
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?	Jun 25, 2023	MRI Reconstruction	CodeCode Available	1	5
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction	Apr 3, 2020	Image ReconstructionMRI Reconstruction	CodeCode Available	1	5
Federated Learning of Generative Image Priors for MRI Reconstruction	Feb 8, 2022	Federated LearningMRI Reconstruction	CodeCode Available	1	5
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models	Dec 8, 2023	DenoisingImage Denoising	CodeCode Available	1	5
Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction	Feb 6, 2025	Federated LearningMRI Reconstruction	CodeCode Available	1	5
Data augmentation for deep learning based accelerated MRI reconstruction with limited data	Jun 28, 2021	Data AugmentationImage Restoration	CodeCode Available	1	5
Deep Low-rank plus Sparse Network for Dynamic MR Imaging	Oct 26, 2020	compressed sensingMRI Reconstruction	CodeCode Available	1	5
High-Frequency Space Diffusion Models for Accelerated MRI	Aug 10, 2022	DenoisingImage Generation	CodeCode Available	1	5
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems	Mar 10, 2023	CT ReconstructionMRI Reconstruction	CodeCode Available	1	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