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 51–60 of 441 papers

Title	Date	Tasks	Status	Hype
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction	Mar 15, 2022	AnatomyMRI Reconstruction	CodeCode Available	1
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
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction	Mar 5, 2022	DenoisingImage Reconstruction	CodeCode Available	1
Federated Learning of Generative Image Priors for MRI Reconstruction	Feb 8, 2022	Federated LearningMRI Reconstruction	CodeCode Available	1
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models	Feb 3, 2022	MRI Reconstruction	CodeCode Available	1
Fast MRI Reconstruction: How Powerful Transformers Are?	Jan 23, 2022	Generative Adversarial NetworkMRI Reconstruction	CodeCode Available	1
ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer	Jan 23, 2022	Feature CorrelationMRI Reconstruction	CodeCode Available	1
Swin Transformer for Fast MRI	Jan 10, 2022	MRI Reconstruction	CodeCode Available	1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction	Nov 30, 2021	DenoisingImage Reconstruction	CodeCode Available	1
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction	Nov 18, 2021	Anatomycompressed sensing	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