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.
Multi -mask self -supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging,
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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.