Numerical Study of Bubble Area Evolution During Acoustic Droplet Vaporization-Enhanced HIFU Treatment

Acoustic droplet vaporization has the potential to shorten treatment time of high-intensity focused ultrasound (HIFU) while minimizing the possible effects of microbubbles along the propagation path. Distribution of the bubbles formed from the droplets during the treatment is the major factor shaping the therapeutic region. A numerical model was proposed to simulate the bubble area evolution during this treatment. Using a linear acoustic equation to describe the ultrasound field, a threshold range was defined that determines the amount of bubbles vaporized in the treated area. Acoustic parameters, such as sound speed, acoustic attenuation coefficient, and density, were treated as a function of the bubble size distribution and the gas void fraction, which were related to the vaporized bubbles in the medium. An effective pressure factor was proposed to account for the influence of the existing bubbles on the vaporization of the nearby droplets. The factor was obtained by fitting one experimental result and was then used to calculate bubble clouds in other experimental cases. Comparing the simulation results to these other experiments validated the model. The dynamic change of the pressure and the bubble distribution after exposure to over 20 pulses of HIFU are obtained. It is found that the bubble area grows from a grainlike shape to a "tadpole," with comparable dimensions and shape to those observed in experiments. The process was highly dynamic with the shape of the bubble area changing with successive HIFU pulses and the focal pressure. The model was further used to predict the shape of the bubble region triggered by HIFU when a bubble wall pre-exists. The results showed that the bubble wall helps prevent droplet vaporization on the distal side of the wall and forms a particularly shaped region with bubbles. This simulation model has predictive potential that could be beneficial in applications, such as cancer treatment, by parametrically studying conditions associated with these treatments and designing treatment protocols.