Signal prediction for magnetic particle imaging using a model-based dictionary approach

Abstract

Magnetic particle imaging (MPI) is a tracer-based medical imaging technique that enables quantification and spatial mapping of magnetic nanoparticle (MNP) distribution. The magnetization response of MNPs depends on both experimental conditions such as drive field (DF) settings and viscosity of the medium, and the magnetic parameters such as magnetic core diameter, hydrodynamic diameter, and magnetic anisotropy constant. A comprehensive understanding of the magnetization response of MNPs can facilitate the optimization of DF and MNP type for a given MPI application. This thesis proposes a calibration-free algorithm using model-based dictionaries for MNP signal prediction at untested experimental conditions. The proposed algorithm also incorporates non-model-based dynamics by modeling them as a linear time-invariant system. These dynamics include the system response of the measurement setup as well as the magnetization dynamics not accounted for by the employed coupled Brown-N´eel rotation model, such as dipolar interactions and non-uniaxial magnetic anisotropy. The proposed iterative calibration-free algorithm simultaneously estimates the dictionary weights and the transfer functions due to non-model based dynamics. Experiments on in-house magnetic particle spectrometer (MPS) setup demonstrate that the pro-posed algorithm successfully predicts the MNP signals at untested viscosities within the biologically relevant range, as well as at untested DF settings.