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 181–190 of 441 papers

Title	Date	Tasks	Status	Score
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems	Nov 16, 2020	DenoisingMRI Reconstruction	CodeCode Available	5
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction	Jul 18, 2022	MRI Reconstruction	CodeCode Available	5
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative Priors	Jul 31, 2024	DenoisingMRI Reconstruction	CodeCode Available	5
AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI Reconstruction	Jan 1, 2025	MRI Reconstruction	CodeCode Available	5
An Unsupervised Deep Learning Method for Multi-coil Cine MRI	Dec 20, 2019	Deep LearningMRI Reconstruction	CodeCode Available	5
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities	Aug 4, 2020	Image ReconstructionMRI Reconstruction	CodeCode Available	5
Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariance	Oct 11, 2024	MRI Reconstruction	CodeCode Available	5
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representation	Sep 12, 2023	GPUMRI Reconstruction	CodeCode Available	5
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks	Mar 13, 2021	compressed sensingDeep Learning	CodeCode Available	5
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge	Feb 4, 2021	MRI ReconstructionSSIM	CodeCode Available	5

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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