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 341–350 of 441 papers

Title	Date	Tasks	Status	Hype
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
GRAPPA-GANs for Parallel MRI Reconstruction	Jan 5, 2021	Generative Adversarial NetworkMRI Reconstruction	—Unverified	0
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction	Jan 5, 2021	MRI Reconstruction	CodeCode Available	1
Data augmentation for deep learning based accelerated MRI reconstruction	Jan 1, 2021	Data AugmentationDeep Learning	—Unverified	0
Active Deep Probabilistic Subsampling	Jan 1, 2021	Deep LearningMRI Reconstruction	—Unverified	0
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine	Dec 14, 2020	compressed sensingImage Reconstruction	—Unverified	0
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction	Dec 9, 2020	BIG-bench Machine LearningImage Reconstruction	CodeCode Available	1
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction	Dec 9, 2020	BIG-bench Machine Learningcompressed sensing	—Unverified	0
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery	Nov 27, 2020	AnatomyMRI Reconstruction	—Unverified	0

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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