Multi-Patch Sequences in Magnetic Particle Imaging

In this project we develop multi-patch imaging sequences and reconstruction algorithms for enlarged measuring fields in magnetic particle imaging (MPI). The regular field-of-view (FOV) in MPI is limited due to physiological constraints such as tissue heating and nerve stimulation. In practice typical FOV are in the range of 2x2x1 cm³. In order to scan larger regions it is possible to shift the FOV to different positions and scan various smaller FOV, which can later be combined to a joint 3D dataset. Especially the reconstruction of multi-patch data is a computationally intensive and memory demanding task. In this project we develop algorithms for efficient reconstruction of multi-patch MPI data.

To reduce calibration time and speed up image reconstruction, we have introduced a number of different methods, including reducing the number of system matricessystem matrix warping, and overscan extrapolation.

Sketch of a multi-patch imaging sequence.

Publications

[178600]
Title: Extrapolation of System Matrices in Magnetic Particle Imaging.
Written by: K. Scheffler, M. Boberg, and T. Knopp
in: <em>IEEE Transactions on Medical Imaging</em>. April (2023).
Volume: <strong>42</strong>. Number: (4),
on pages: 1121 - 1132
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DOI: 10.1109/TMI.2022.3224310
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[pdf]

Note: article, multi-patch, artifact, openaccess

Abstract: Magnetic particle imaging exploits the non-linear magnetization of superparamagnetic iron-oxide particles to generate a tomographic image in a defined field-of-view. For reconstruction of the particle distribution, a time-consuming calibration step is required, in which system matrices get measured using a robot. To achieve artifact-free images, system matrices need to cover not only the field-of-view but also a larger area around it. Especially for large measurements – inevitable for future clinical application – this leads to long calibration time and high consumption of persistent memory. In this work, we analyze the signal in the outer part of the system matrix and motivate the usage of extrapolation methods to computationally expand the system matrix after restricting the calibration to the field-of-view. We propose a suitable extrapolation method and show its applicability on measured 2D and 3D data. In doing so, we achieve a considerable reduction of calibration time and consumption of persistent memory while preserving an artifact-free result.