Research on Imaging Algorithm of Industrial Pipeline Detection Based on List-Mode Data

In order to realize the y-photon nondestructive detection of industrial confined pipelines, it is necessary to construct large axial ring y-photon detectors, but with the increase of the axial length of the detectors, the acquisition of response lines will be truncated and missing, resulting in the degradation of the detection imaging quality in large axial space. In this paper, we propose a list-mode data-based imaging algorithm for industrial confined pipe detection, which makes full use of the list-mode to conform to the position and time information in the event and to reasonably assign the weights of the system matrix in the image reconstruction. When calculating the weight contribution of the pixels passing through a response line to that response line, only the pixels within the uncertainty range need to be calculated without the pixels corresponding to the complete response line, and the system matrix containing time-of-flight (TOF) information is obtained, thus effectively suppressing the noise caused by truncated and missing large axial spatial data. In addition, the parallel feature of CUDA is also used to divide the system matrix calculation process into small blocks that are independent of each other to achieve accelerated optimization of the algorithm. Finally, two industrial models are used for simulation experiments, and the experimental results show that the proposed method can significantly improve the resolution and spatial contrast of large axial spatial reconstruction images in industrial confined pipeline inspection, and can meet the demand of y-photon nondestructive inspection of industrial confined pipelines.