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 81–90 of 441 papers

Title	Date	Tasks	Status	Hype
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction	Apr 3, 2020	Image ReconstructionMRI Reconstruction	CodeCode Available	1
Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction	May 11, 2023	MRI Reconstruction	CodeCode Available	1
A Neural-Network-Based Convex Regularizer for Inverse Problems	Nov 22, 2022	DenoisingImage Reconstruction	CodeCode Available	1
Is good old GRAPPA dead?	Jun 1, 2021	MRI Reconstruction	CodeCode Available	1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow	Apr 11, 2020	CPUGPU	CodeCode Available	1
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise	Nov 16, 2023	Deep LearningImage Generation	CodeCode Available	1
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction	Aug 2, 2023	MRI Reconstruction	CodeCode Available	1
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms	Aug 21, 2023	Image ReconstructionMRI Reconstruction	CodeCode Available	1
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction	Mar 5, 2022	DenoisingImage Reconstruction	CodeCode Available	1
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning	Sep 30, 2021	compressed sensingDenoising	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