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.
K-band: self-supervised MRI reconstruction via stochastic gradient descent over k-space subsets
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NexOP jointly optimizes NEX-aware k-space sampling probabilities and multi-measurement reconstruction to raise effective SNR in low-field MRI under a fixed total sampling budget.
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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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NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI
NexOP jointly optimizes NEX-aware k-space sampling probabilities and multi-measurement reconstruction to raise effective SNR in low-field MRI under a fixed total sampling budget.