Information-Theoretic Analysis of Multimodal Image Translation

Area of Science:

• Medical Imaging
• Information Theory
• Machine Learning

Background:

• Multimodal image translation is crucial for medical imaging challenges.
• Existing methods lack a quantitative information-theoretic understanding.
• Analyzing mutual information across modalities is essential.

Purpose of the Study:

• To systematically analyze multimodal medical images from an information-theoretic perspective.
• To quantify information transfer and gain in machine learning-based image translation.
• To develop information-theoretic measures for evaluating image translation effectiveness and uncertainty.

Main Methods:

• Information-theoretic analysis of mutual information in common multimodal images.
• Quantification of information transfer and gain in image translation.
• Development of novel information-theoretic metrics for translator assessment.
• Numerical validation of theoretical findings and proposed bounds.

Main Results:

• Identified varying structural correlations and tissue-dependence of mutual information across modalities.
• Quantified information gain in practical multimodal image translation.
• Established an upper bound for information gain in image translation.
• Validated the proposed upper bound and translation error predictor.

Conclusions:

• Information-theoretic analysis provides valuable insights into multimodal image translation.
• Proposed measures can effectively assess image translator performance and uncertainty.
• Findings can guide the development of advanced medical imaging techniques like denoising and reconstruction.