Open Access Publications

The Institute's work is published in both traditional journals (e.g. the prestigious imaging journal IEEE Transactions on Medical Imaging) and open access journals. For traditional journals, a preprint is uploaded to ArXiv whenever possible to make the research results freely available.

In addition, Tobias Knopp, as Editor-in-Chief, has founded a new scientific Open Access journal, which makes all articles available under the Creative Commons License (CC-BY-4.0). The International Journal on MagneticParticle Imaging (IJMPI) was founded in 2015 and publishes new research developments within the MPI community.

Open Access Publications

[134633]
Title: Towards accurate modeling of the multidimensional magnetic particle imaging physics.
Written by: T. Kluth, P. Szwargulski, and T. Knopp
in: <em>New Journal of Physics</em>. 10 (2019).
Volume: <strong>21</strong>. Number: (10),
on pages: 103032
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DOI: 10.1088/1367-2630/ab4938
URL: https://doi.org/10.1088%2F1367-2630%2Fab4938
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Note: article, openaccess, model-based

Abstract: The image reconstruction problem of the tomographic imaging technique magnetic particle imaging (MPI) requires the solution of a linear inverse problem. One prerequisite for this task is that the imaging operator that describes the mapping between the tomographic image and the measured signal is accurately known. For 2D and 3D excitation patterns, it is common to measure the system matrix in a calibration procedure, that is both, very time consuming and adds noise to the operator. The need for measuring the system matrix is due to the lack of an accurate model that is capable of describing the nanoparticles’ magnetization behavior in the MPI setup. Within this work we exploit a physical model that is based on Néel rotation for large particle ensembles and we find model parameters that describe measured 2D MPI data with much higher precision than state of the art MPI models. With phantom experiments we show that the simulated system matrix can be used for image reconstruction and reduces artifacts due to model-mismatch considerably.