A multi-contrast self-supervised MRI reconstruction framework with end-to-end learned k-space partitioning produces higher-fidelity images than single-contrast self-supervised baselines on two public datasets.
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2026 2verdicts
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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.
citing papers explorer
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Optimized Multi-Contrast Self-Supervised MRI Reconstruction using Learned k-space Partitioning
A multi-contrast self-supervised MRI reconstruction framework with end-to-end learned k-space partitioning produces higher-fidelity images than single-contrast self-supervised baselines on two public datasets.
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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.