Objectives: Monitoring cavitation during ultrasound therapy is crucial for assessing the procedure safety and efficacy. This work aims to develop a self-sensing and low-complexity approach for robust cavitation detection in moving organs such as the heart.
Methods: An analog-to-digital converter was connected onto one channel of the therapeutic transducer from a clinical system dedicated to cardiac therapy, allowing to record signals on a computer. Acquisition of successive echoes backscattered by the cavitation cloud on the therapeutic transducer was performed at a high repetition rate. Temporal variations of the backscattered echoes were analyzed with a Singular-Value Decomposition filter to discriminate signals associated to cavitation, based on its stochastic nature. Metrics were derived to classify the filtered backscattered echoes. Classification of raw backscattered echoes was also performed with a machine learning approach. The performances were evaluated on 155 in vitro acquisitions and 110 signals acquired in vivo during transthoracic cardiac ultrasound therapy on 3 swine.
Results: Cavitation detection was achieved successfully in moving tissues with high signal to noise ratio in vitro (cSNR = 25±5) and in vivo (cSNR = 20±6) and outperformed conventional methods (cSNR = 11±6). Classification methods were validated with spectral analysis of hydrophone measurements. High accuracy was obtained using either the clutter filter-based method (accuracy of 1) or the neural network-based method (accuracy of 0.99).
Conclusion: Robust self-sensing cavitation detection was demonstrated to be possible with a clutter filter-based method and a machine learning approach.
Significance: The self-sensing cavitation detection method enables robust, reliable and low complexity cavitation activity monitoring during ultrasound therapy.