Image reconstruction of ultrasonic transmission tomography using spectral-based graph convolution network

Abstract Ultrasonic transmission tomography (UTT) is a crucial imaging modality for non-destructive testing and quality assessment in industrial settings. The reconstruction of high-quality images from limited, irregular, and noisy measurement of ultrasound measurements poses a significant challenge due to the ill-posed nature of the inverse problem. Therefore, the proposed method involves formulating the UTT image reconstruction problem into a graph-based learning task. In this task, nodes symbolize locations on the inspected object, while edges illustrate the connections between those locations. Furthermore, it employs the graph Laplacian function to inject graph structure into the learning algorithm to extract the structural details of the acoustic parameter distribution from the ultrasound measurements. The spectral graph convolutional neural networks (SGCNNs) enforce smoothness across the graph. The unbiased risk estimation-based spectral approach efficiently shapes the irregularity graph structure. The proposed SGCNN is tailored to effectively learn and exploit the complex relationships between transmitted ultrasound measurements and the underlying substantial properties in an ultrasound spectrum. Simulation and experimental tests are carried out to validate the proposed algorithm’s performance. In addition, the SGCNN algorithm is compared to graph convolution neural network and traditional reconstruction methods, showcasing its superior ability to reconstruct detailed and accurate images by evaluating performance metrics.