Switchable deep beamformer for high-quality and real-time passive acoustic mapping

TL;DR

This paper introduces a deep learning-based switchable beamformer for passive acoustic mapping that achieves high-quality imaging with significantly reduced computational cost, enabling real-time monitoring of cavitation activities in ultrasound therapy.

Contribution

A novel deep beamformer based on GANs that can switch between transducer arrays and reconstruct high-quality PAM images efficiently.

Findings

Reduces energy spread area by up to 65%.

Improves signal-to-noise ratio by up to 22.9 dB.

Achieves 10.5 ms reconstruction speed, comparable image quality to traditional methods.

Abstract

Passive acoustic mapping (PAM) is a promising tool for monitoring acoustic cavitation activities in the applications of ultrasound therapy. Data-adaptive beamformers for PAM have better image quality compared to the time exposure acoustics (TEA) algorithms. However, the computational cost of data-adaptive beamformers is considerably expensive. In this work, we develop a deep beamformer based on a generative adversarial network, which can switch between different transducer arrays and reconstruct high-quality PAM images directly from radio frequency ultrasound signals with low computational cost. The deep beamformer was trained on the dataset consisting of simulated and experimental cavitation signals of single and multiple microbubble clouds measured by different (linear and phased) arrays covering 1-15 MHz. We compared the performance of the deep beamformer to TEA and three different data-adaptive beamformers using the simulated and experimental test dataset. Compared with TEA, the deep beamformer reduced the energy spread area by 18.9%-65.0% and improved the image signal-to-noise ratio by 9.3-22.9 dB in average for the different arrays in our data. Compared to the data-adaptive beamformers, the deep beamformer reduced the computational cost by three orders of magnitude achieving 10.5 ms image reconstruction speed in our data, while the image quality was as good as that of the data-adaptive beamformers. These results demonstrated the potential of the deep beamformer for high-resolution monitoring of microbubble cavitation activities for ultrasound therapy.