Application of PET imaging based on DOI information in nondestructive testing

• Published in: Measurement : journal of the International Measurement Confederation 2024-05, Vol.231, p.114662, Article 114662
• Main Authors: Yao, Min, Ma, Bolin, Shan, Yao, Zhao, Min, Guo, Ruipeng, Wang, Ming
• Format: Article
• Language: English
• Subjects: Depth of interaction; Image enhancement; Nondestructive testing; Oil flow method; Positron emission tomography imaging
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.114662

•The proposed algorithm compensates for the collected line of response data;•A set of wind tunnel oil flow method experiments is designed;•This algorithm improves image edge recognition and restoration. Images from positron emission tomography (PET) for non-destructive testing of industrial cavities have low resolution and blurred edges. This study proposes an algorithm based on depth of interaction information to improve the image edge recognition and restoration. A synchronous iterative filter–maximum likelihood expectation maximisation (SIF–MELM) algorithm is proposed based on the traditional MLEM algorithm to improve the imaging quality. A set of engine blade simulation models is designed to verify the performance of the algorithm. Image quality evaluations are conducted on the images reconstructed by the algorithm before and after improvement. A set of wind tunnel oil flow experiments are designed to verify the effectiveness and superiority of this method. Experimental results show that, the peak signal-to-noise and structural similarity of the reconstructed images increase from 23.99 and 0.60 to 27.37 and 0.73, respectively. Moreover, the oil flow trajectory conforms to the simulation results.