Semi-Analytical Super-Resolution X-Space Reconstruction for Magnetic Particle Imaging Scanner via Adaptive Kernel Optimization

Magnetic Particle Imaging (MPI) is an emerging biomedical imaging technique. The x-space method, one of the mainstream reconstruction methods in MPI, offers high efficiency and real-time capabilities but is limited by theoretical spatial resolution constraints and typically necessitates high gradient magnetic fields. This study introduces a semi-analytical reconstruction (Semi-AR) method for x-space MPI scanner, incorporating a kernel optimization step to achieve a spatial resolution better than the theoretical limit. By modeling the xspace MPI system with focus-field sequences as a linear shift invariant system, the point spread function (PSF) is decomposed into basis functions and variants across different spatial frequencies. These functions are weighted to reconstruct a highresolution PSF, with optimal weights adaptively determined via quadratic programming. A mouse-sized MPI scanner with 3D focus-field sequences was developed to evaluate the method. Simulation and experimental results showcase Semi-AR's superior spatial resolution and robustness compared to existing x-space techniques, particularly in detecting low-brightness targets near highlighted non-target organs. Both phantom and in vivo experiments robustly validate Semi-AR's effectiveness, providing new insights into MPI scanner development, and advancing preclinical and potential clinical MPI applications.