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 231–240 of 441 papers

Title	Date	Tasks	Status
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representation	Sep 12, 2023	GPUMRI Reconstruction	CodeCode Available
Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification	Sep 11, 2023	Adversarial DefenseImage Reconstruction	CodeCode Available
Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction	Sep 2, 2023	MRI ReconstructionTAR	CodeCode Available
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction	Sep 2, 2023	DenoisingImage Reconstruction	—Unverified
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data	Aug 21, 2023	DeblurringDenoising	CodeCode Available
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction	Aug 20, 2023	Federated LearningMRI Reconstruction	—Unverified
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems	Aug 16, 2023	3D Volumetric ReconstructionComputed Tomography (CT)	CodeCode Available
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning	Aug 15, 2023	AnatomyDeep Learning	—Unverified
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction	Aug 9, 2023	AnatomyImage Reconstruction	CodeCode Available
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets	Aug 5, 2023	MRI 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