UNITS framework proves self-supervised splitting risk in MRI reconstruction is a weighted supervised risk, yielding identical Bayes-optimal predictors and relating training residuals to prediction bias.
Zero-shot self-supervised learning for mri reconstruction,
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DMSM proposes a self-supervised dual-domain multi-path diffusion framework for accelerated MRI reconstruction that removes the need for fully sampled training data while providing uncertainty maps.
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Towards a Unified Theoretical Framework for Splitting-based Self-Supervised MRI Reconstruction
UNITS framework proves self-supervised splitting risk in MRI reconstruction is a weighted supervised risk, yielding identical Bayes-optimal predictors and relating training residuals to prediction bias.
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Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction
DMSM proposes a self-supervised dual-domain multi-path diffusion framework for accelerated MRI reconstruction that removes the need for fully sampled training data while providing uncertainty maps.