Identifying Unique Acoustic Signatures from Chemically-Crosslinked Microbubble Clusters Using Deep Learning

Ultrasound contrast agents (UCA) are gas encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing a backscattered signal which can be used for improved ultrasound imaging and drug delivery. UCA's are being used widely for contrast-enhanced ultrasound imaging, but there is a need for improved UCAs to develop faster and more accurate contrast agent detection algorithms. Recently, we introduced a new class of lipid based UCAs called Chemically Cross-linked Microbubble Clusters (CCMCs). CCMCs are formed by the physical tethering of individual lipid microbubbles into a larger aggregate cluster. The advantages of these novel CCMCs are their ability to fuse together when exposed to low intensity pulsed ultrasound (US), potentially generating unique acoustic signatures that can enable better contrast agent detection. In this study, our main objective is to demonstrate that the acoustic response of CCMCs is unique and distinct when compared to individual UCAs using deep learning algorithms. Acoustic characterization of CCMCs and individual bubbles was performed using a broadband hydrophone or a clinical transducer attached to a Verasonics Vantage 256. A simple artificial neural network (ANN) was trained and used to classify raw 1D RF ultrasound data as either from CCMC or non-tethered individual bubble populations of UCAs. The ANN was able to classify CCMCs at an accuracy of 93.8% for data collected from broadband hydrophone and 90% for data collected using Verasonics with a clinical transducer. The results obtained suggest the acoustic response of CCMCs is unique and has the potential to be used in developing a novel contrast agent detection technique.