Dual-functional magnetic nanoparticles: A stochastic Langevin study of imaging-heating trade-offs

Magnetic nanoparticles (MNPs) are central to magnetic particle imaging (MPI) and magnetic hyperthermia. The intrinsic properties of MNPs critically determine the performance of each modality individually. The integration of imaging and therapy within a single MPI-hyperthermia platform is especially attractive, as it enables mapping of MNP tracer distribution using MPI prior to thermal treatment, thereby guiding the subsequent delivery of localized hyperthermia. In such dual-functional systems, however, nanoparticle properties play a more complex and potentially different role than in standalone applications. Understanding how these intrinsic characteristics govern the sequential imaging and heating performance within a unified platform is therefore essential for the rational design of effective theranostic systems. Nevertheless, the impact of the nanoparticles’ inherent characteristics on achieving optimal imaging and heating performance across both imaging and therapeutic stages has not yet been thoroughly investigated. In this work, we present a comprehensive modeling-based framework to optimize dual-functional MPI-hyperthermia performance by systematically investigating three MNP core sizes (20, 25, and 30 nm) over broad ranges of anisotropy constants and saturation magnetizations using a stochastic Langevin model. Magnetization dynamics are evaluated at 25 kHz (for MPI use) and 350 kHz (for hyperthermia use) alternating magnetic fields. Performance is quantified using the full width at half-maximum and signal-to-noise ratio to assess MPI spatial resolution and signal quality, respectively, and the specific absorption rate to evaluate hyperthermia efficiency. The competing behavior of these performance metrics is first analyzed through direct visualization of the simulated data, revealing clear trade-offs between spatial resolution, signal quality, and heating efficiency. To capture the underlying nonlinear relationships more rigorously, Gaussian process surrogate models are constructed for each metric and validated for predictive accuracy. Building on these models, a multiobjective optimization framework is employed to identify optimal combinations of anisotropy constant and saturation magnetization for each core size that balance MPI and hyperthermia performance. The results demonstrate a strong dependence of the optimal parameter regions on MNP core size, with distinct trade-offs emerging between imaging and therapeutic objectives. This study provides a systematic and extensible framework for the rational design of dual-functional MNPs and offers quantitative guidance for optimizing particle properties in combined MPI-guided magnetic hyperthermia applications.