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 401–410 of 441 papers

Title	Date	Tasks	Status
Deep Plug-and-Play Prior for Parallel MRI Reconstruction	Aug 30, 2019	Image ReconstructionMRI Reconstruction	—Unverified
Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction	Aug 25, 2019	DiagnosticGenerative Adversarial Network	CodeCode Available
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset	Aug 24, 2019	MRI ReconstructionSSIM	—Unverified
Efficient Structurally-Strengthened Generative Adversarial Network for MRI Reconstruction	Aug 11, 2019	compressed sensingDecoder	—Unverified
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks	Jul 26, 2019	Deep LearningImage Reconstruction	—Unverified
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction	Jul 19, 2019	compressed sensingDeep Learning	CodeCode Available
Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging	Jun 18, 2019	MRI Reconstruction	—Unverified
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network	Jun 11, 2019	compressed sensingMRI Reconstruction	—Unverified
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction	May 2, 2019	Generative Adversarial NetworkMRI Reconstruction	—Unverified
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space	Apr 20, 2019	Deep LearningMRI Reconstruction	—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