Modeling Magnetic Particle Imaging for Dynamic Tracer Distributions

TL;DR

This paper introduces a new forward model for Magnetic Particle Imaging that accounts for dynamic tracer concentrations, aiming to improve image reconstruction accuracy during changing conditions.

Contribution

The paper presents an extended MPI forward model capable of handling dynamic tracer concentrations, addressing a key limitation of existing static models.

Findings

The extended model captures concentration changes during scanning.

Example reconstructions show improved accuracy with the new model.

The model's relevance is demonstrated through comparative analysis.

Abstract

Magnetic Particle Imaging (MPI) is a promising tracer-based, functional medical imaging technique which measures the non-linear magnetization response of magnetic nanoparticles to a dynamic magnetic field. For image reconstruction, system matrices from time-consuming calibration scans are used predominantly. Finding modeled forward operators for magnetic particle imaging, which are able to compete with measured matrices in practice, is an ongoing topic of research. The existing models for magnetic particle imaging are by design not suitable for arbitrary dynamic tracer concentrations. Neither modeled nor measured system matrices account for changes in the concentration during a single scanning cycle. In this paper we present a new MPI forward model for dynamic concentrations. A static model will be introduced briefly, followed by the changes due to the dynamic behavior of the tracer concentration. Furthermore, the relevance of this new extended model is examined by investigating the influence of the extension and example reconstructions with the new and the standard model.