Harnessing chemically crosslinked microbubble clusters using deep learning for ultrasound contrast imaging.

Abstract

Purpose: We aim to investigate and isolate the distinctive acoustic properties generated by chemically crosslinked microbubble clusters (CCMCs) using machine learning (ML) techniques, specifically using an anomaly detection model based on autoencoders.

Approach: CCMCs were synthesized via copper-free click chemistry and subjected to acoustic analysis using a clinical transducer. Radiofrequency data were acquired, processed, and organized into training and testing datasets for the ML models. We trained an anomaly detection model with the nonclustered microbubbles (MBs) and tested the model on the CCMCs to isolate the unique acoustics. We also had a separate set of control experiments that was performed to validate the anomaly detection model.

Results: The anomaly detection model successfully identified frames exhibiting unique acoustic signatures associated with CCMCs. Frequency domain analysis further confirmed that these frames displayed higher amplitude and energy, suggesting the occurrence of potential coalescence events. The specificity of the model was validated through control experiments, in which both groups contained only individual MBs without clustering. As anticipated, no anomalies were detected in this control dataset, reinforcing the model's ability to distinguish clustered MBs from nonclustered ones.

Conclusions: We highlight the feasibility of detecting and distinguishing the unique acoustic characteristics of CCMCs, thereby improving the detectability and localization of contrast agents in ultrasound imaging. The elevated acoustic amplitudes produced by CCMCs offer potential advantages for more effective contrast agent detection, which is particularly valuable in super-resolution ultrasound imaging. Both the contrast agent and the ML-based analysis approach hold promise for a wide range of applications.