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 11–20 of 441 papers

Title	Date	Tasks	Status	Hype
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning	Sep 21, 2024	MRI Reconstruction	CodeCode Available	1
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling	Aug 11, 2024	DenoisingImage Denoising	CodeCode Available	1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant Attention	Jul 19, 2024	compressed sensingDenoising	CodeCode Available	1
IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural Representations	Jul 3, 2024	DiagnosticImage Reconstruction	CodeCode Available	1
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI	May 23, 2024	MRI Reconstruction	CodeCode Available	1
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models	May 9, 2024	Medical DiagnosisMRI Reconstruction	CodeCode Available	1
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging	Apr 30, 2024	BenchmarkingImage Reconstruction	CodeCode Available	1
Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI Reconstruction	Mar 23, 2024	compressed sensingImage Generation	CodeCode Available	1
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI	Mar 15, 2024	MRI Reconstruction	CodeCode Available	1
TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method	Feb 17, 2024	Image GenerationMRI Reconstruction	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