Iterative reconstruction of industrial positron images with generative networks.

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 itera

SUBMITTER: Zhu M 

PROVIDER: S-EPMC12629474 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature