Artifact Reduction for MPI

High-quality images are essential for any imaging modality to make a reliable diagnosis, and although MPI is highly sensitive, artifacts are common. This issue poses significant challenges for applications that operate in environments with extremely low levels of iron, such as cell tracking. As a result, our objective is to reduce the amount of image artifacts in MPI by implementing different methods in the reconstruction process that allow for these applications. Key components for artifact reduction are:

Extrapolating the system matrix beyond the drive-field field of view reduces artifacts at the patch boundaries in multi-patch imaging scenarios.

Publications

[132355]
Title: Generalized MPI Multi-Patch Reconstruction using Clusters of similar System Matrices.
Written by: M. Boberg, T. Knopp, P. Szwargulski, and M. Möddel
in: <em>IEEE Transactions on Medical Imaging</em>. May (2020).
Volume: <strong>39</strong>. Number: (5),
on pages: 1347-1358
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.1109/TMI.2019.2949171
URL: https://arxiv.org/abs/2205.01083
ARXIVID:
PMID:

[www]

Note: article, multi-patch, artifact, magneticfield, openaccess

Abstract: The tomographic imaging method magnetic particle imaging (MPI) requires a multi-patch approach for capturing large field of views. This approach consists of a continuous or stepwise spatial shift of a small sub-volume of only few cubic centimeters size, which is scanned using one or multiple excitation fields in the kHz range. Under the assumption of ideal magnetic fields, the MPI system matrix is shift invariant and in turn a single matrix suffices for image reconstruction significantly reducing the calibration time and reconstruction effort. For large field imperfections, however, the method can lead to severe image artifacts. In the present work we generalize the efficient multi-patch reconstruction to work under non-ideal field conditions, where shift invariance holds only approximately for small shifts of the sub-volume. Patches are clustered based on a magnetic-field-based metric such that in each cluster the shift invariance holds in good approximation. The total number of clusters is the main parameter of our method and allows to trade off calibration time and image artifacts. The magnetic-field-based metric allows to perform the clustering without prior knowledge of the system matrices. The developed reconstruction algorithm is evaluated on a multi-patch measurement sequence with 15 patches, where efficient multi-patch reconstruction with a single calibration measurement leads to strong image artifacts. Analysis reveals that calibration measurements can be decreased from 15 to 11 with no visible image artifacts. A further reduction to 9 is possible with only slight degradation in image quality.