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 431–440 of 441 papers

Title	Date	Tasks	Status
Deep learning for undersampled MRI reconstruction	Sep 8, 2017	Deep LearningMRI Reconstruction	—Unverified
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss	Sep 3, 2017	compressed sensingGenerative Adversarial Network	CodeCode Available
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation	Jul 22, 2017	compressed sensingImage Reconstruction	CodeCode Available
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI	May 31, 2017	compressed sensingDiagnostic	CodeCode Available
On the Empirical Effect of Gaussian Noise in Under-sampled MRI Reconstruction	Oct 3, 2016	Image ReconstructionMRI Reconstruction	—Unverified
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields	Dec 25, 2015	Compressive SensingMRI Reconstruction	—Unverified
Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation	Nov 20, 2015	AnatomyMRI Reconstruction	—Unverified
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder	Mar 22, 2015	compressed sensingDenoising	—Unverified
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction	Mar 10, 2015	Dictionary LearningImage Reconstruction	—Unverified
Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction	Nov 23, 2014	Compressive SensingMRI Reconstruction	—Unverified

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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