Parameter identification for the Landau-Lifshitz-Gilbert equation in Magnetic Particle Imaging

TL;DR

This paper formulates and analyzes the inverse problem of parameter identification for the Landau-Lifshitz-Gilbert equation, crucial for accurate magnetic particle imaging calibration, providing theoretical insights and a foundation for numerical methods.

Contribution

It introduces a detailed formulation and analysis of the inverse parameter identification problem for the LLG equation in MPI calibration, including all-at-once and reduced models.

Findings

Deeper understanding of inverse problems related to the LLG equation.

Analytical results support development of robust numerical methods.

Framework applicable to MPI system function calibration.

Abstract

Magnetic particle imaging (MPI) is a tracer-based technique for medical imaging where the tracer consists of ironoxide nanoparticles. The key idea is to measure the particle response to a temporally changing external magnetic field to compute the spatial concentration of the tracer inside the object. A decent mathematical model demands for a data-driven computation of the system function which does not only describe the measurement geometry but also encodes the interaction of the particles with the external magnetic field. The physical model of this interaction is given by the Landau-Lifshitz-Gilbert (LLG) equation. The determination of the system function can be seen as an inverse problem of its own which can be interpreted as a calibration problem for MPI. In this contribution the calibration problem is formulated as an inverse parameter identification problem for the LLG equation. We give a detailed analysis of the direct as well as the inverse problem in an all-at-once as well as in a reduced setting. The analytical results yield a deeper understanding of inverse problems connected to the LLG equation and provide a starting point for the development of robust numerical solution methods in MPI.