Generation of System Function Maps in Projection-Based Magnetic Particle Imaging Using Lock-in-Amplifier Model

TL;DR

This paper presents a simulation-based method to generate system function maps in projection-based magnetic particle imaging, aiding in understanding and optimizing the imaging process by analyzing various influencing parameters.

Contribution

The study introduces a novel simulation approach using a lock-in-amplifier model to generate system function maps in projection-based MPI, considering multiple physical parameters.

Findings

System functions are well represented by the generated maps.

Particle size, medium viscosity, and magnetic field parameters significantly affect system functions.

The method enhances understanding and potential optimization of MPI systems.

Abstract

We previously developed a system for projection-based magnetic particle imaging (MPI) with a field-free-line (FFL) encoding scheme. In the projection-based MPI, projection data are given by the convolution between the system function in the spatial domain and the line integral of the concentration of magnetic nanoparticles (MNPs) through the FFL. Thus, it is important to estimate the system functions and to investigate the factors affecting them for enhancing the quantitative property of the projection-based MPI. The purpose of this study was to present a method for generating the system function maps in projection-based MPI. In the simulation studies, the MPI signals induced by MNPs in a receiving coil were calculated using a lock-in-amplifier model under the assumption that the magnetization and particle size distribution of MNPs obey the Langevin theory of paramagnetism and a log-normal distribution, respectively. The system function maps were generated by calculating the MPI signals at various distances from the FFL and angles between the axis of the receiving coil and the selection magnetic field. The effects of the particle size of MNPs, the viscosity of the suspending medium, the amplitude of the drive magnetic field, and the gradient strength of the selection magnetic field on the system function were investigated. The spatial distributions of the system functions could be well understood from their maps generated by our method and were largely affected by the parameters described above. Our method will be useful for improved understanding, optimization, and development of projection-based MPI.