Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Age-Specific Cerebral Vessel Templates Across the Lifespan of Healthy Adults.

Scientific data·2026

Same author

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Denoising of ASL Data Using Deep Learning Priors Generated From Distribution Remapping.

Magnetic resonance in medicine·2026

Same author

Temporal-Spatial Fusion Vision Hardware Enables Streamlined In-Sensor Computing for Dynamic Scenes.

Nature communications·2026

Same author

Unsupervised Brain Lesion Segmentation Using Posterior Distributions Learned by Subspace-Based Generative Model.

IEEE transactions on medical imaging·2025

Same author

Are environmental levels of gabapentin (GBP) a cause for concern? Chronic reproductive effects of GBP in zebrafish.

Journal of hazardous materials·2025

Search research articles

Related Experiment Video

Updated: May 15, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Information-Theoretic Analysis of Multimodal Image Translation.

Ruihao Liu, Yudu Li, Yao Li

IEEE Transactions on Medical Imaging

|April 10, 2025

Summary

This summary is machine-generated.

This study analyzes multimodal medical image translation using information theory. We quantified information gain and proposed new measures to assess image translation effectiveness and uncertainty.

More Related Videos

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Published on: December 8, 2010

Related Experiment Videos

Last Updated: May 15, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Published on: December 8, 2010

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.