Reconstruction Algorithm for Regions of Interest in γ-Photon Images

• Main Authors: Min Yao, Gang Lv, Min Zhao, Ruipeng Guo, Jian Liu, Dawei Zhen, Ming Wang, Fang Xiong, Wei Liu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: γ-photon imaging; OSEM; ROI; system matrix; industrial nondestructive testing
• Online Access: https://ieeexplore.ieee.org/document/9429257/

As a nondestructive testing technology, <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-photon imaging shows immense potential in the industrial field. However, the limitations of <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-photon imaging theory and detection technology result in various problems, such as low image resolution and edge blur. The technology is particularly difficult to apply to industrial detection that requires high imaging speed and high resolution. Therefore, this study proposes a reconstruction algorithm for regions of interest (ROI) in <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-photon images. The proposed algorithm is suitable for fast industrial detection and is based on the reconstruction algorithm for sinusoidal graph data, that is, the ordered subset expectation maximization (OSEM) image reconstruction algorithm. It is an improvement of the traditional point-and-line system matrix (SM) model. In the application of the proposed algorithm, the probability weight of a pixel is determined by the solid angle of the crystal bar at both ends of the line of response (LOR) to the pixel it passes through. In this work, the known contour parameters of industrial parts are used to describe the area of nuclide distribution as the ROI. Only the pixels through which the LOR passed in the ROI are counted, and the probability weights of these pixels are calculated to construct the SM. Gaussian filters are added in each iteration to suppress the clutter of scattered noise inside the image. The effectiveness of the algorithm was verified in two model experiments. A closed cavity detection experiment on industrial hydraulic parts was also conducted to compare the image reconstruction effects before and after the improvement. Results showed that the proposed algorithm can effectively improve image resolution and image edge contours. In the tee pipe model experiment and cavity detection experiment on hydraulic parts, the image reconstruction speed increased by more than 6 and 10 times, respectively. Hence, the proposed algorithm provided a feasible solution for quickly obtaining images with clear edges and high resolution under a large aperture detector ring.