Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging

TL;DR

This paper introduces an information-theoretic framework for task-adapted compressed sensing MRI that enables probabilistic inference, adaptive sampling, and improved uncertainty quantification across diverse clinical applications.

Contribution

It formalizes the optimization of CS-MRI by maximizing mutual information, allowing flexible sampling and addressing uncertainty in clinical diagnosis.

Findings

Achieves competitive Dice scores compared to deterministic methods.

Provides better distribution matching to ground-truth posterior as measured by GED.

Supports both joint task-reconstruction and privacy-preserving scenarios.

Abstract

Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or clinical tasks. To address these limitations, we propose the first task-adapted CS-MRI from the information-theoretic perspective to simultaneously achieve probabilistic inference for uncertainty prediction and adapt to arbitrary sampling ratios and versatile clinical applications. Specifically, we formalize the task-adapted CS-MRI optimization problem by maximizing the mutual information between undersampled k-space measurements and clinical tasks to enable probabilistic inference for addressing the uncertainty problem. We leverage amortized optimization and construct tractable variational bounds for mutual information to jointly optimize sampling, reconstruction, and task-inference models, which enables flexible sampling ratio control using a single end-to-end trained model. Furthermore, the proposed framework addresses two kinds of distinct clinical scenarios within a unified approach, i.e., i) joint task and reconstruction, where reconstruction serves as an auxiliary process to enhance task performance; and ii) task implementation with suppressed reconstruction, applicable for privacy protection. Extensive experiments on large-scale MRI datasets demonstrate that the proposed framework achieves highly competitive performance on standard metrics like Dice compared to deterministic counterpart but provides better distribution matching to the ground-truth posterior distribution as measured by the generalized energy distance (GED).