Acoustic emission-based estimation of weakly nonlinear microbubble dynamics

Monitoring microbubble (MB) dynamics is critical during MB-enhanced focused ultrasound (FUS) interventions; our ability to understand distinct mechano-biological effects related to MB dynamics hinges on our ability to accurately estimate the temporal MB radius changes during ultrasonic excitation. Here, we hypothesize that acoustic signatures recorded from real-time passive cavitation detection (PCD) can be used to estimate weakly nonlinear MB dynamics. To test our hypothesis, we employed numerical simulations, based on Rayleigh–Plesset modeling, followed by experimental validation, using calibrated PCDs with concurrent optical imaging of MB dynamics using high frame rate microscopy. Our method, termed linear acoustic wave propagation and superposition (LAWPS) algorithm, is derived using Fourier-series-based linearization of the relationship between MB (which is considered as a monopole source, R0 + ΔR < λ) oscillation and pressure propagation. In our Rayleigh–Plesset-based numerical simulation, LAWPS algorithm estimation closely corroborated the current state-of-the-art non-linear estimation. Crucially, the LAWPS algorithm provided a method to estimate the MB oscillation radius using AE. Moreover, experimental observation using monodisperse MBs and high frame rate microscopy supported the potential of the LAWPS algorithm to accurately estimate the temporal changes in MB radius during 0.5MHz ultrasonic excitation for small and stable (ΔR/R0 < 30%) oscillations.