The classifier judged this item not relevant to AI security, so it is left out of lists, feeds, the API and dataset releases. If that is wrong, report a correction on this page.
Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging
- Published
- Record updated
Summary
The paper proposes the first task-adapted compressed sensing MRI (CS-MRI) method built on information-theoretic principles, which maximizes mutual information between undersampled k-space measurements and clinical tasks to enable probabilistic inference and uncertainty prediction. A single end-to-end trained model adapts to arbitrary sampling ratios and supports both joint task-and-reconstruction and reconstruction-suppressed settings for privacy protection. On large-scale MRI datasets, it reaches competitive Dice scores against a deterministic counterpart and better matches the ground-truth posterior distribution as measured by generalized energy distance (GED).