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 391–400 of 441 papers

Title	Date	Tasks	Status
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction	Dec 1, 2019	compressed sensingMRI Reconstruction	CodeCode Available
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)	Nov 27, 2019	Deep LearningMRI Reconstruction	—Unverified
Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation Discretization	Nov 26, 2019	Image ReconstructionMRI Reconstruction	CodeCode Available
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction	Oct 27, 2019	compressed sensingMRI Reconstruction	—Unverified
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data	Oct 21, 2019	MRI ReconstructionSelf-Supervised Learning	CodeCode Available
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks	Oct 14, 2019	Compressive SensingGenerative Adversarial Network	CodeCode Available
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings	Oct 4, 2019	MRI Reconstruction	—Unverified
Time-Dependent Deep Image Prior for Dynamic MRI	Oct 3, 2019	MRI Reconstruction	CodeCode Available
pISTA-SENSE-ResNet for Parallel MRI Reconstruction	Sep 24, 2019	Deep LearningImage Reconstruction	—Unverified
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network	Sep 19, 2019	MRI 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