Mathematical models for magnetic particle imaging

TLDR: In this article, a survey of deterministic and non-deterministic models for magnetic particle imaging is presented, which are based on the physical behavior including relaxation mechanisms affecting the particle magnetization.

Abstract: Magnetic particle imaging (MPI) is a relatively new imaging modality. The nonlinear magnetization behavior of nanoparticles in an applied magnetic field is employed to reconstruct an image of the concentration of nanoparticles. Finding a sufficiently accurate model for the particle behavior is still an open problem. For this reason the reconstruction is still computed using a measured forward operator which is obtained in a time-consuming calibration process. The state of the art model used for the imaging methodology and first model-based reconstructions relies on strong model simplifications which turned out to cause too large modeling errors. Neglecting particle-particle interactions, the forward operator can be expressed by a Fredholm integral operator of the first kind describing the inverse problem. In this article we give an overview of relevant mathematical models which have not been investigated theoretically in the context of inverse problems yet. We consider deterministic models which are based on the physical behavior including relaxation mechanisms affecting the particle magnetization. The behavior of the models is illustrated with numerical simulations for monodisperse as well as polydisperse tracer. We further motivate linear and nonlinear problems beyond the solely concentration reconstruction related to applications. This model survey complements a recent topical review on MPI [30] and builds the basis for upcoming theoretical as well as empirical investigations.