Switchable Deep Beamformer for High-quality and Real-time Passive Acoustic Mapping.
Authors
Affiliations (5)
Affiliations (5)
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
- Gene Editing Center, School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China. Electronic address: [email protected].
- Gene Editing Center, School of Life Science and Technology, ShanghaiTech University, Shanghai, China; State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China. Electronic address: [email protected].
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China; Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai Tech University, Shanghai, China; Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China. Electronic address: [email protected].
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 with 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 that can switch between different transducer arrays and reconstruct high-quality PAM images directly from radiofrequency ultrasound signals with low computational cost. The deep beamformer was trained on a 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 simulated and experimental test dataset. Compared with TEA, the deep beamformer reduced the energy spread area by 27.3%-77.8% and improved the image signal-to-noise ratio by 13.9-25.1 dB on average for the different arrays in our data. Compared with 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 demonstrate the potential of the deep beamformer for high-resolution monitoring of microbubble cavitation activities for ultrasound therapy.