结合空化微泡母小波变换的高空化噪声比超快速 主动空化成像方法*

本文提出一种结合空化微泡母小波技术(Cavitation Bubble Wavelet Transform, CBWT)、波束合成和平方差值求和减影(Sum-of-Squared Differences, SSD)的超快速主动空化成像方法。该方法首先发射平面波并接收空化回波信号；其次,基于RPNNP模型构建空化微泡母小波,对空化回波信号进行连续小波变换；再次,对获得的小波系数进行波束合成,包括延迟叠加算法(Delay-and-Sum, DAS)、最小方差算法(Minimum-Variance, MV)和相干系数最小方差算法(Coherence-Factor-based-Minimum-Variance, MVCF),再结合SSD,得到空化图像。结果表明,与未采用CBWT相比,基于CBWT-DAS-SSD、CBWT-MV-SSD和CBWT-MVCF-SSD的空化噪声比分别提高了16.34 dB、15.07 dB、17.71 dB。本文方法可提高空化成像质量,为空化动态实时监控提供参考。

An ultrafast active cavitation imaging with high cavitation-to-noise ratio based on cavitation bubble wavelet transform

In this paper, an ultrafast active cavitation imaging combined cavitation bubble wavelet (CBWT), beamform algorithm and sum-of-squared differences (SSD) algorithm is proposed. Firstly, plane wave pulse is transmited and cavitation echo signal is received. Secondly, based on RPNNP model, the cavitation bubble mother wavelet is constructed and continuous wavelet transform is performed on cavitation echo signal. Thirdly, the obtained wavelet coefficients are beformed by delay-and-sum algorithm (DAS), minimum-variance algorithm (MV) and coherence coefficient minimum variance algorithm (MVCF). Furthermore, SSD algorithm is used to obtain the cavitation image. The results showed that compared with cavitation images without CBWT, the cavitation-to-noise ratios of cavitation images based on CBWT-DAS-SSD, CBWT-MV-SSD and CBWT-MVCF-SSD increased by 16.34 dB, 15.07 dB and 17.71 dB, respectively. The method in this paper can improve the quality of cavitation image, and may provide reference for dynamic real-time monitoring of cavitation bubbles.