A Progressive Transformer-GAN Framework for System Matrix Recovery in Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is an emerging non-invasive high-resolution technique, but its practicality is constrained by time-consuming repetitive calibration of the System Matrix (SM) when parameters, particle types, or environments change. To address this, we propose TP-GAN, a Transformer-based Progressive GAN for MPI SM super-resolution. It integrates a feature enhancement module to stabilize SM’s physical structure and capture cross-scale correlations, with multi-loss optimization improving consistency between super-resolution and real high-resolution SM, as well as accuracy and anti-noise performance. Experimental results show TP-GAN outperforms existing methods, reducing reliance on repeated calibration and advancing MPI’s biomedical applications.