Enhanced modelling of a 1-D phased ultrasonic array for intracorporeal sonoporation

Moldovan, Alexandru C. and Qiu, Zhen and Lines, David and Gachagan, Anthony and Cochran, Sandy; (2020) Enhanced modelling of a 1-D phased ultrasonic array for intracorporeal sonoporation. In: 2020 IEEE International Ultrasonics Symposium (IUS). IEEE International Ultrasonics Symposium . IEEE, Piscataway, NJ.. ISBN 9781728154497 (https://doi.org/10.1109/IUS46767.2020.9251375)

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Precise control of ultrasonic (US) power is required during sonoporation to ensure pressure amplitude in the target tissue is maintained within the bounds of therapeutic efficiency and below the maximum threshold of safe use. Pressure mapping with a scanning tank and needle hydrophone (NH) represents a convenient means to evaluate US beam profiles and peak negative pressures (PNP) achieved by therapeutic arrays in the medium. However, the method is restricted to fluid media only, and lacks the dynamic capabilities required during the medical procedure. Software modelling can address these issues for dynamic determination of the optimum array driving parameters in relation to therapeutic requirements. This paper describes the correlation between measurements obtained with a scanning tank and the simulation results for four different experimental 1-D phased arrays. Two simulation frameworks were developed using OnScale (Onscale, CA, US) and cross-compared, with the main difference between them being the solver used for modelling US propagation in the medium. The first method used a finite element analysis (FEA) approach for the water and the second method, previously described in [1], relied on Kirchhoff time-domain extrapolation. Electrical cabling was first omitted then included in the model and the outputs were compared with the previous data. Results show that both frameworks led to a similar fit in PNP amplitude with the measurements and that the beam shape strongly agrees with the experimental data in both cases. However, the extrapolation-based method was less computationally demanding than FEA and allowed for modelling larger loads within available computing resources i.e. memory.