Suppression of Motion Artifacts Caused by Temporally Recurring Tracer Distributions in Multi-Patch Magnetic Particle Imaging

TL;DR

This paper introduces a raw data processing method for multi-patch magnetic particle imaging that suppresses motion artifacts caused by periodic physiological motions, improving image quality during dynamic tracer imaging.

Contribution

A novel data processing technique that combines raw data snippets into virtual frames, effectively reducing motion artifacts in magnetic particle imaging without additional navigation signals.

Findings

Effective suppression of motion artifacts in phantom experiments

Ability to determine rotational frequencies directly from raw data

Improved image quality of dynamic tracer distributions

Abstract

Magnetic particle imaging is a tracer based imaging technique to determine the spatial distribution of superparamagnetic iron oxide nanoparticles with a high spatial and temporal resolution. Due to physiological constraints, the imaging volume is restricted in size and larger volumes are covered by shifting object and imaging volume relative to each other. This results in reduced temporal resolution, which can lead to motion artifacts when imaging dynamic tracer distributions. A common source of such dynamic distributions are cardiac and respiratory motion in in-vivo experiments, which are in good approximation periodic. We present a raw data processing technique that combines data snippets into virtual frames corresponding to a specific state of the dynamic motion. The technique is evaluated on the basis of measurement data obtained from a rotational phantom at two different rotational frequencies. These frequencies are determined from the raw data without reconstruction and without an additional navigator signal. The reconstructed images give reasonable representations of the rotational phantom frozen in several different states of motion while motion artifacts are suppressed.