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 31–40 of 441 papers

Title	Date	Tasks	Status	Hype
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting	Mar 3, 2025	Computational EfficiencyDenoising	CodeCode Available	1
Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty	Feb 28, 2025	MRI ReconstructionQuantitative MRI	—Unverified	0
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction	Feb 28, 2025	Bilevel OptimizationMRI Reconstruction	—Unverified	0
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching	Feb 24, 2025	compressed sensingDenoising	—Unverified	0
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation	Feb 23, 2025	DenoisingImage Denoising	—Unverified	0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction	Feb 19, 2025	BenchmarkingMRI Reconstruction	CodeCode Available	0
JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRI	Feb 17, 2025	MRI Reconstruction	CodeCode Available	0
Three-Dimensional MRI Reconstruction with Gaussian Representations: Tackling the Undersampling Problem	Feb 10, 2025	3DGSMRI Reconstruction	—Unverified	0
Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction	Feb 6, 2025	Federated LearningMRI Reconstruction	CodeCode Available	1
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration	Jan 24, 2025	compressed sensingFederated Learning	CodeCode Available	2

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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