Passive cavitation mapping using dual apodization with cross-correlation in ultrasound therapy monitoring

Recently, passive acoustic mapping (PAM) has been successfully applied for dynamic monitoring of ultrasound therapy by beamforming acoustic emissions of cavitation activity during ultrasound exposure. The most widely used PAM algorithm in the literature is time exposure acoustics (TEA), which is a standard delay, sum, and integrate algorithm. However, it results in large point spread function (PSF) and serious imaging artifacts for the case where a narrow-aperture receiving array such as a standard B-mode linear array is used, therefore degrading the quality of cavitation image. To address these challenges, in this paper, we proposed a novel PAM algorithm namely dual apodization with cross-correlation (DAX)-based TEA, in which DAX was originally used as a reconstruction algorithm in medical ultrasound imaging. In the proposed algorithm, two sets of signals were beamformed by two receive apodization functions with alternating elements enabled, and the cross-correlation coefficient of the two signals served as a weighting factor that would be multiplied to the sum of the two signals. The performance of the proposed algorithm was tested on simulated channel data obtained using a multi-bubble model, and experiments were also performed in an in vitro vessel phantom with flowing microbubbles as cavitation nuclei. The reconstructed cavitation images were evaluated quantitatively using established quality metrics including full width at half maximum (FWHM), A_−6dB area, and signal-to-noise ratio (SNR). The results suggested that the proposed algorithm significantly outperformed the conventionally used TEA algorithm. This work may have the potential of providing a useful tool for highly accurate localization of cavitation activity during ultrasound therapy.