Computational Predictions on the Magnetic Particle Imaging Performance of Tracers Subjected to Magnetic Dipole–Dipole Interactions

Magnetic particle imaging (MPI) is a tracer-based tomographic imaging technique recognized for its tissue-depth-independent sensitivity, quantitative accuracy, and high spatial and temporal resolution. Superparamagnetic iron oxide nanoparticle (SPION) tracers are widely used in MPI research for applications such as cancer imaging, cardiovascular imaging, stem cell tracking, drug delivery monitoring. As MPI transitions toward clinical use, several challenges remain to be addressed, including tracer design, image reconstruction algorithms, and optimized field configurations, to achieve improved imaging performance. The magnetic dipole-dipole interactions (dipolar interactions) between magnetized tracers are often overlooked in applications such as magnetic sensing, imaging, and separation. However, in imaging scenarios where tracers target tumors, stem cells, or similar applications, surface-functionalized tracers tend to aggregate at target sites, leading to reduced interparticle distances where dipolar fields from neighboring tracers become significant. This study investigates the impact of magnetic dipolar interactions among SPION tracers on MPI imaging performance by tuning the inter-particle distances. Using the stochastic Langevin function, we modeled an ensemble of 1000 SPION tracers within a field-free region (FFR). Key magnetization dynamics such as time-domain magnetization responses, magnetization-field response (M–H) curves, voltage signals recorded by receiver coils, higher harmonics, and point spread functions (PSFs) were analyzed. Here, higher harmonics and the full width at half maximum (FWHM) of the PSFs serve as indicators for evaluating the impact of dipolar interactions on MPI performance.