An information-theoretic optimization framework for task-adapted CS-MRI enables adaptive sampling at arbitrary ratios and probabilistic inference for uncertainty while supporting joint reconstruction-task or privacy-focused scenarios.
J-MoDL: Joint model-based deep learning for optimized sampling and reconstruction,
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Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging
An information-theoretic optimization framework for task-adapted CS-MRI enables adaptive sampling at arbitrary ratios and probabilistic inference for uncertainty while supporting joint reconstruction-task or privacy-focused scenarios.