Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Diagnostic Ultrasound

Background:

• Ultrasound contrast agents (UCAs) are microbubbles used in dynamic contrast-enhanced ultrasound (DCE-US) imaging.
• UCAs mimic red blood cells, serving as intravascular indicators for microvasculature imaging.
• Quantitative DCE-US analysis aids in diagnosing various pathological conditions.

Purpose of the Study:

• To comprehensively review quantitative DCE-US imaging methods for UCA kinetics analysis.
• To discuss advancements in UCA technology and kinetic modeling for molecular imaging.
• To explore the integration of machine learning for improved diagnostic accuracy.

Main Methods:

• Review of temporal, spatial, and spatiotemporal analysis of UCA kinetics.
• Analysis of kinetic models for quantitative DCE-US imaging.
• Evaluation of machine learning frameworks for integrating complementary DCE-US data.

Main Results:

• DCE-US with UCAs provides functional imaging of the vasculature.
• Novel targeted UCAs enable molecular imaging applications.
• Advanced kinetic models and machine learning improve diagnostic accuracy.

Conclusions:

• Quantitative DCE-US imaging offers significant diagnostic potential.
• The integration of advanced kinetic models and machine learning represents the future of DCE-US.
• Further research is needed to fully leverage these advancements for clinical applications.