CoilDrop-MRI uses coil dropout in self-supervised training for parallel MRI reconstruction and outperforms prior self-supervised methods while matching supervised quality across multi-site, multi-field, and multi-modality data.
SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Realistic noise synthesis incorporating Rician expectation and effective variance into simulated training data reduces bias in supervised ML for diffusion MRI microstructure estimation.
citing papers explorer
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CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout
CoilDrop-MRI uses coil dropout in self-supervised training for parallel MRI reconstruction and outperforms prior self-supervised methods while matching supervised quality across multi-site, multi-field, and multi-modality data.
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Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning
Realistic noise synthesis incorporating Rician expectation and effective variance into simulated training data reduces bias in supervised ML for diffusion MRI microstructure estimation.