Model-Based Reconstruction for Magnetic Particle Imaging

TLDR

Magnetic particle imaging (MPI) is a new modality that offers high sensitivity, spatial resolution, and imaging speed, but its reconstruction relies on a system function that is traditionally acquired through a tedious calibration grid. This study aims to compute the MPI system function for the first time using a model of the signal chain. The modeled system function enables reconstruction of particle distributions in a 1‑D MPI experiment. The model-based approach allows rapid generation of system functions on arbitrarily dense grids and may reduce memory usage by computing parts on the fly during reconstruction.

Abstract

Magnetic particle imaging (MPI) is a new imaging modality capable of imaging distributions of superparamagnetic nanoparticles with high sensitivity, high spatial resolution and, in particular, high imaging speed. The image reconstruction process requires a system function, describing the mapping between particle distribution and acquired signal. To date, the system function is acquired in a tedious calibration procedure by sequentially measuring the signal of a delta sample at the positions of a grid that covers the field of view. In this work, for the first time, the system function is calculated using a model of the signal chain. The modeled system function allows for reconstruction of the particle distribution in a 1-D MPI experiment. The approach thus enables fast generation of system functions on arbitrarily dense grids. Furthermore, reduction in memory requirements may be feasible by generating parts of the system function on the fly during reconstruction instead of keeping the complete matrix in memory.