Hui Xiao, Quan Liu, YiBing Xu, Ming Wang, JianTang Liu
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing, China.
Sci Rep. 2025 Jul 1;15(1):21443. doi: 10.1038/s41598-025-07107-1.
Positron emission tomography (PET) technology, with its advantages of strong γ-photon penetration and results unaffected by temperature or electromagnetic fields, has emerged as a novel non-contact monitoring technique for industrial flow fields under harsh conditions. However, dynamic sampling leads to a severe lack of photon data within individual time frames, resulting in an ill-posed nature of positron image reconstruction, which introduces uncertainty in noise statistical characteristics and degradation in imaging quality. This paper proposes a novel noise-suppressing super-resolution enhancement module for positron flow field imaging. The module, based on convolution and SwinTransformer structures, achieves noise reduction and enhancement of positron images under conditions of severe photon scarcity. Furthermore, a multi-loss fusion performance evaluation system is constructed to extract texture and hierarchical feature information from the images. Experimental results demonstrate that the proposed module effectively reduces image noise while preserving critical information, achieving significant improvements in the quality of generated positron flow field images.
正电子发射断层扫描（PET）技术凭借其γ光子穿透能力强以及结果不受温度或电磁场影响的优势，已成为一种用于恶劣条件下工业流场的新型非接触监测技术。然而，动态采样导致单个时间帧内光子数据严重不足，从而造成正电子图像重建的不适定性，这在噪声统计特性方面引入了不确定性，并导致成像质量下降。本文提出了一种用于正电子流场成像的新型噪声抑制超分辨率增强模块。该模块基于卷积和SwinTransformer结构，在光子严重稀缺的条件下实现了正电子图像的降噪和增强。此外，构建了一个多损失融合性能评估系统，以从图像中提取纹理和层次特征信息。实验结果表明，所提出的模块在保留关键信息的同时有效降低了图像噪声，在生成的正电子流场图像质量方面取得了显著提升。
