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 361–370 of 441 papers

Title	Date	Tasks	Status
GRAPPA-GANs for Parallel MRI Reconstruction	Jan 5, 2021	Generative Adversarial NetworkMRI Reconstruction	—Unverified
Data augmentation for deep learning based accelerated MRI reconstruction	Jan 1, 2021	Data AugmentationDeep Learning	—Unverified
Active Deep Probabilistic Subsampling	Jan 1, 2021	Deep LearningMRI Reconstruction	—Unverified
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine	Dec 14, 2020	compressed sensingImage Reconstruction	—Unverified
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction	Dec 9, 2020	BIG-bench Machine Learningcompressed sensing	—Unverified
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery	Nov 27, 2020	AnatomyMRI Reconstruction	—Unverified
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems	Nov 16, 2020	DenoisingMRI Reconstruction	CodeCode Available
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations	Nov 10, 2020	Experimental DesignImage Reconstruction	CodeCode Available
Risk Quantification in Deep MRI Reconstruction	Oct 23, 2020	MRI Reconstruction	—Unverified
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding	Oct 8, 2020	Data CompressionImage Compression	—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