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 421–430 of 441 papers

Title	Date	Tasks	Status
Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data	Dec 16, 2019	compressed sensingMRI Reconstruction	CodeCode Available
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction	Dec 1, 2019	compressed sensingMRI Reconstruction	CodeCode Available
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data	Oct 21, 2019	MRI ReconstructionSelf-Supervised Learning	CodeCode Available
Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization	Dec 15, 2023	Image InpaintingImage Reconstruction	CodeCode Available
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU	Oct 4, 2022	DenoisingMRI Reconstruction	CodeCode Available
Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis	Mar 2, 2023	compressed sensingImage Reconstruction	CodeCode Available
Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction	Dec 26, 2021	CT ReconstructionMRI Reconstruction	CodeCode Available
Deep Geometric Distillation Network for Compressive Sensing MRI	Jul 11, 2021	compressed sensingCompressive Sensing	CodeCode Available
CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction	Jun 20, 2023	Image EnhancementImage Reconstruction	CodeCode Available
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction	May 17, 2021	Image ReconstructionMRI Reconstruction	CodeCode Available

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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