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一种基于高阶奇异值分解的超声逆时偏移成像方法。

An Ultrasonic Reverse Time Migration Imaging Method Based on Higher-Order Singular Value Decomposition.

作者信息

Zhang Yuncheng, Gao Xiang, Zhang Jiawei, Jiao Jingpin

机构信息

Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.

出版信息

Sensors (Basel). 2022 Mar 25;22(7):2534. doi: 10.3390/s22072534.

DOI:10.3390/s22072534
PMID:35408150
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9002699/
Abstract

An ultrasonic reverse time migration imaging method, based on high-order singular value decomposition, is proposed in the study to solve the problems of low signal-to-noise ratio (SNR) and excessive artifacts in defect ultrasonic detection imaging results of materials with high noise levels. In this method, based on the 3D structural properties of the ultrasonic full-matrix capture data, higher-order singular value decomposition is directly performed with the 3D data. The method overcomes the difficulty in selecting the number of singular values in the original singular value decomposition noise-reduction algorithm and realizes the one-step noise reduction processing of all the signals. Subsequently, the reverse time migration imaging is performed in the frequency domain, and high-quality acoustic images are obtained. The effects of the number of array elements, the center frequency of the excitation signal, and the number of defects on the denoising effect of the algorithm are investigated. It was experimentally demonstrated that the method could suppress the interference of noise signals and significantly improve the imaging SNR compared with total focusing method and the reverse time migration.

摘要

本研究提出了一种基于高阶奇异值分解的超声逆时偏移成像方法，以解决高噪声水平材料缺陷超声检测成像结果中信噪比低和伪像过多的问题。在该方法中，基于超声全矩阵采集数据的三维结构特性，直接对三维数据进行高阶奇异值分解。该方法克服了原奇异值分解降噪算法中奇异值数量选择的困难，实现了对所有信号的一步降噪处理。随后，在频域中进行逆时偏移成像，获得了高质量的声学图像。研究了阵列元件数量、激励信号中心频率和缺陷数量对算法降噪效果的影响。实验证明，与全聚焦方法和逆时偏移相比，该方法能够抑制噪声信号的干扰，显著提高成像信噪比。

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