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 61–70 of 441 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Robust Training of Deep Learning MRI Reconstruction Models	Oct 30, 2020	Deep LearningImage Reconstruction	CodeCode Available	1	5
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI	May 23, 2024	MRI Reconstruction	CodeCode Available	1	5
Deep Low-rank plus Sparse Network for Dynamic MR Imaging	Oct 26, 2020	compressed sensingMRI Reconstruction	CodeCode Available	1	5
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant Attention	Jul 19, 2024	compressed sensingDenoising	CodeCode Available	1	5
Federated Learning of Generative Image Priors for MRI Reconstruction	Feb 8, 2022	Federated LearningMRI Reconstruction	CodeCode Available	1	5
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets	Mar 6, 2020	BenchmarkingImage Reconstruction	CodeCode Available	1	5
Adaptive Diffusion Priors for Accelerated MRI Reconstruction	Jul 12, 2022	De-aliasingMRI Reconstruction	CodeCode Available	1	5
Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction	Feb 6, 2025	Federated LearningMRI 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
Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction	May 11, 2023	MRI 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