# Iterative reconstruction of industrial positron images with generative networks

## TL;DR

This paper introduces a new method using generative networks to improve the quality of industrial positron images, especially when data is limited.

## Contribution

The novel approach integrates a generative adversarial network with iterative reconstruction and a likelihood-based constraint for better image quality.

## Key findings

- The method shows significant improvements in PSNR and SSIM on simulated datasets.
- Real-world industrial defect detection confirms the method's effectiveness.
- The self-attention mechanism enhances performance under low-sample conditions.

## 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.