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 71–80 of 441 papers

Title	Date	Tasks	Status	Hype
Is good old GRAPPA dead?	Jun 1, 2021	MRI Reconstruction	CodeCode Available	1
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers	May 15, 2021	Image ReconstructionMRI Reconstruction	CodeCode Available	1
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization	Mar 2, 2021	compressed sensingDeep Learning	CodeCode Available	1
Zero-Shot Self-Supervised Learning for MRI Reconstruction	Feb 15, 2021	AnatomyMRI Reconstruction	CodeCode Available	1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brain	Jan 19, 2021	Data AugmentationDimensionality Reduction	CodeCode Available	1
Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks	Jan 6, 2021	compressed sensingMRI Reconstruction	CodeCode Available	1
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction	Jan 5, 2021	MRI Reconstruction	CodeCode Available	1
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction	Dec 9, 2020	BIG-bench Machine LearningImage Reconstruction	CodeCode Available	1
Learning Multiscale Convolutional Dictionaries for Image Reconstruction	Nov 25, 2020	Dictionary LearningImage Reconstruction	CodeCode Available	1
Adversarial Robust Training of Deep Learning MRI Reconstruction Models	Oct 30, 2020	Deep LearningImage 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