Classification and prediction of inertial cavitation activity induced by pulsed high-intensity focused ultrasound

Abstract

Classification and prediction of ultrasound-induced microbubble inertial cavitation (IC) activity may play an important role in better design of ultrasound treatment strategy with improved efficiency and safety. Here, a new method was proposed by combining support vector machine (SVM) algorithm with passive cavitation detection (PCD) measurements to fulfill the tasks of IC event classification and IC dose prediction. By using the PCD system, IC thresholds and IC doses were firstly measured for various ultrasound contrast agent (UCA) solutions exposed to pulsed high-intensity focused ultrasound (pHIFU) at different driving pressures and pulse lengths. Then, after trained and tested by measured data, two SVM models (viz. C-SVC and ε-SVR) were established to classify the likelihood of IC event occurrence and predict IC dose, respectively, under different parameter conditions. The findings of this study indicate that the combination of SVM and PCD could be used as a useful tool to optimize the operation strategy of cavitation-facilitated pHIFU therapy.