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 341–350 of 441 papers

Title	Date	Tasks	Status
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization	Jul 24, 2021	MRI Reconstruction	—Unverified
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss	Jul 21, 2021	Generative Adversarial NetworkMRI Reconstruction	—Unverified
Deep Geometric Distillation Network for Compressive Sensing MRI	Jul 11, 2021	compressed sensingCompressive Sensing	CodeCode Available
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction	Jul 7, 2021	MRI Reconstruction	—Unverified
Over-and-Under Complete Convolutional RNN for MRI Reconstruction	Jun 16, 2021	compressed sensingImage Reconstruction	—Unverified
Sparse recovery based on the generalized error function	May 26, 2021	MRI Reconstruction	—Unverified
Covariance-Free Sparse Bayesian Learning	May 21, 2021	compressed sensingMRI Reconstruction	—Unverified
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI	May 19, 2021	Image ReconstructionMRI Reconstruction	—Unverified
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning	May 17, 2021	Deep LearningMRI Reconstruction	—Unverified
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