文献类型：期刊文献

英文题名：A multiscale residual U-net architecture for super-resolution ultrasonic phased array imaging from full matrix capture data

作者：Liu, Lishuai[1];Liu, Wen[1];Teng, Da[1];Xiang, Yanxun[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China

年份：2023

卷号：154

期号：4

起止页码：2044

外文期刊名：JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA

收录：;EI(收录号：20234114856774);WOS:【SCI-EXPANDED(收录号:WOS:001082256600008)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant No. 2022YFF0605600), the National Natural Science Foundation of China (Grant Nos. 12104155 and 12025403), the Shanghai Chenguang Program (Grant No. 21CGA36), and the Natural Science Foundation of Shanghai (Grant No. 21ZR1417100). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The data that support the findings of this study are available from the corresponding author upon reasonable request.

语种：英文

外文关键词：Deep learning - Matrix algebra - Ultrasonic imaging - Ultrasonic testing

摘要：Ultrasonic phased array imaging using full-matrix capture (FMC) has raised great interest among various communities, including the nondestructive testing community, as it makes full use of the echo space to provide preferable visualization performance of inhomogeneities. The conventional way of FMC data postprocessing for imaging is through beamforming approaches, such as delay-and-sum, which suffers from limited imaging resolution and contrast-to-noise ratio. To tackle these difficulties, we propose a deep learning (DL)-based image forming approach, termed FMC-Net, to reconstruct high-quality ultrasonic images directly from FMC data. Benefitting from the remarkable capability of DL to approximate nonlinear mapping, the developed FMC-Net automatically models the underlying nonlinear wave-matter interactions; thus, it is trained end-to-end to link the FMC data to the spatial distribution of the acoustic scattering coefficient of the inspected object. Specifically, the FMC-Net is an encoder-decoder architecture composed of multiscale residual modules that make local perception at different scales for the transmitter-receiver pair combinations in the FMC data. We numerically and experimentally compared the DL imaging results to the total focusing method and wavenumber algorithm and demonstrated that the proposed FMC-Net remarkably outperforms conventional methods in terms of exceeding resolution limit and visualizing subwavelength defects. It is expected that the proposed DL approach can benefit a variety of ultrasonic array imaging applications. (c) 2023 Acoustical Society of America.