Image Reconstruction in Ultrasonic Speed-of-Sound Computed Tomography Using Time of Flight Estimated by a 2D Convolutional Neural Networks

Abstract

In ultrasonic nondestructive testing (NDT), accurately estimating the time of flight (TOF) of ultrasonic waves is crucial. Traditionally, TOF estimation involves the signal processing of a single measured waveform. In recent years, deep learning has also been applied to estimate the TOF; however, these methods typically process only single waveforms. In contrast, this study acquired fan-beam ultrasonic waveform profile data from 64 paths using an ultrasonic-speed computed tomography (CT) simulation of a circular column and developed a TOF estimation model using two-dimensional convolutional neural networks (CNNs) based on these data. We compared the accuracy of the TOF estimation between the proposed method and two traditional signal processing methods. Additionally, we reconstructed ultrasonic-speed CT images using the estimated TOF and evaluated the generated CT images. The results showed that the proposed method could estimate the longitudinal TOF more accurately than traditional methods, and the evaluation scores for the reconstructed images were high.