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	Hype
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks	Mar 25, 2019	Clusteringcompressed sensing	—Unverified	0
Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation	Mar 7, 2019	Image RestorationMRI Reconstruction	—Unverified	0
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding	Feb 20, 2019	Generative Adversarial NetworkMotion Correction In Multishot Mri	—Unverified	0
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction	Feb 18, 2019	compressed sensingGenerative Adversarial Network	—Unverified	0
Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition	Feb 8, 2019	MRI Reconstruction	CodeCode Available	0
Generalising Deep Learning MRI Reconstruction across Different Domains	Jan 31, 2019	Deep LearningMRI Reconstruction	—Unverified	0
Uncertainty Quantification in Deep MRI Reconstruction	Jan 31, 2019	DiagnosticMRI Reconstruction	—Unverified	0
A Scale Invariant Approach for Sparse Signal Recovery	Dec 20, 2018	MRI Reconstruction	—Unverified	0
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction	Oct 25, 2018	compressed sensingImage Reconstruction	—Unverified	0
MRI Reconstruction via Cascaded Channel-wise Attention Network	Oct 18, 2018	compressed sensingMRI Reconstruction	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