An improved scheme for passive cavitation imaging in high intensity focused ultrasound

Abstract

Passive cavitation imaging (PCI) is an effective technique for monitoring high intensity focused ultrasound (HIFU) procedure due to the correlation between cavitation activities and the therapeutic effects. Nevertheless, current PCI techniques are unable to recognize noise signals from non-cavitation situations, and they fail to accurately visualize the highly dynamic cavitation activities due to the lack of a stable baseline reference. In this study, a deep learning-based cavitation detection network (CD-NET) was proposed to determine whether passively received channel signals originated from cavitation activities or were merely noises. Additionally, a cavitation intensity baseline was derived to better display the drastically time-varying cavitation activities. The CD-NET model was trained and validated with data collected from in-vitro and ex-vivo HIFU sonication experiments, and then tested by in-vivo rabbit experiments. Both CD-NET models, trained with data from the in-vitro agar phantoms and ex-vivo bovine liver tissues, successfully distinguished between cavitation and non-cavitation activities from the same study object, achieving accuracies as high as 99.7% and 99.5%, respectively. In addition, the models trained on in-vitro and ex-vivo datasets achieved accuracies of 88.1% and 91.4%, respectively, when detecting cavitation events from unseen in-vivo data. The results demonstrate that the improved PCI scheme, utilizing the CD-NET model and a data-derived cavitation baseline, is resistant to noise from non-cavitation situations and can depict the dynamic characteristics of cavitation more reliably. In conclusion, this study addressed two practical challenges in PCI techniques, allowing for better monitoring of cavitation-related HIFU sonication in clinical applications.