Iterative reconstruction of industrial positron images with generative networks

Mingwei Zhu, Min Zhao and Min Yao

PLOS ONE, 2025, vol. 20, issue 11, 1-16

Abstract: Positron imaging has shown great potential in industrial non-destructive testing due to its high sensitivity and ability to reveal internal structures of complex components. However, reconstructing high-quality images from positron emission data remains challenging, particularly under limited sampling and ill-posed inverse problems, which are common in applications such as closed cavity detection. To address this, we propose an iterative reconstruction method for industrial positron images based on a generative adversarial network (PIIR-GAN). The method integrates a generative adversarial framework with a self-attention mechanism to exploit prior information and improve image quality under low-sample conditions. A key innovation is embedding the neural network model directly into the iterative reconstruction process, enabling end-to-end learning. Furthermore, a likelihood-based constraint is incorporated into the objective function to guide optimization. Experimental results on a GATE simulation dataset show significant improvements in both PSNR and SSIM compared with conventional methods, and real-world industrial defect detection further verifies the effectiveness of the approach.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335912

DOI: 10.1371/journal.pone.0335912

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