Photoacoustic imaging (PAI) uniquely combines optics and ultrasound, presenting a promising role in biomedical imaging as a non-invasive and label-free imaging technology. As the traditional opaque ultrasound (US) transducers could hinder the transportation of the excitation light and limit the performance of PAI system, piezoelectric transparent ultrasonic transducers (TUTs) with indium tin oxide (ITO) electrodes have been developed to allow light transmission through the transducer and illuminate the sample directly. Nevertheless, without having transparent matching materials with appropriate properties, the bandwidth of those TUTs was generally narrow. In this work, we propose to employ polymethyl methacrylate (PMMA) as the matching layer material to improve the bandwidth of lithium niobate (LN)-based TUTs. The effects of PMMA matching layer on the performance of TUTs have been systematically studied. With the optimized PMMA matching layer, the very wide bandwidth of > 50 % could be achieved for the TUTs even with different transducer frequencies, leading to the great enhancement of axial resolution when compared to the similar reported work. In addition, the imaging performance of the developed TUT prototype has been evaluated in a PAI system and demonstrated by both phantom and in vivo small animal imaging.

With the popularization of humans working in tandem with robots and artificial intelligence (AI) by Industry 5.0, ultrasonic non-destructive testing (NDT)) technology has been increasingly used in quality inspections in the industry. As a crucial part of handling ultrasonic testing results–signal processing, the current approach focuses on professional training to perform signal discrimination but automatic and intelligent signal optimization and estimation lack systematic research. Though the automated and intelligent framework for ultrasonic echo signal processing has already exhibited essential research significance for diagnosing defect locations, the real-time applicability of the algorithm for the time-of-flight (ToF) estimation is rarely considered, which is a very important indicator for intelligent detection. This paper conducts a systematic comparison among different ToF algorithms for the first time and presents the auto-diagnosis of the ToF approach based on the Defect Peaks Tracking Model (DPTM). The proposed DPTM is used for ultrasonic echo signal processing and recognition for the first time. The DPTM using the Hilbert transform was verified to locate the defect with the size of 2–10 mm, in which the wavelet denoising method was adopted. With the designed mechanical fixture through 3D printing technology on the pipeline to inspect defects, the difficulty of collecting sufficient data could be conquered. The maximum auto-diagnosis error could be reduced to 0.25% and 1.25% for steel plate and pipeline under constant pressure, respectively, which were much smaller than those with the DPTM adopting the cross-correlation. The real-time auto-diagnosis identification feature of DPTM has the potential to be combined with AI in future work, such as machine learning and deep learning, to achieve more intelligent approaches for industrial health inspection.