Systems matrix super-resolution of magnetic particle imaging based on generative adversarial networks and attentional...

Magnetic Particle Imaging (MPI), as an emerging non-invasive imaging technique, acquires high-resolution images by detecting the response of magnetic nanoparticles. Reconstruction based on System Matrix (SM) is an important component of MPI research. However, when scanning parameters, particle types, or environmental conditions change, SM requires repetitive and time-consuming calibration scans, significantly reducing its practicality. In this study, we propose a novel SM super-resolution recovery method, PGSM-net, based on Generative Adversarial Network (GAN) and Pyramid Attention Mechanism (PAM) to enhance SM calibration in MPI. The generator utilizes enhanced ResNet blocks and a PAM module for multi-scale feature extraction, attention weighting, and feature fusion to recover finer details from low-resolution inputs. The discriminator, an unpooled VGG network, assesses high-dimensional feature consistency to aid the generator in producing more realistic and higher-quality images. Our approach represents the first application of GAN and perceptual loss in MPI system matrix calibration. Experimental results show significant improvements in SM recovery quality and detail, highlighting its potential in MPI applications.