International Journal on Magnetic Particle Imaging IJMPI
Vol. 9 No. 1 Suppl 1 (2023): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2023.2303004
A Dictionary-Based Algorithm for MNP Signal Prediction at Unmeasured Drive Field Frequencies
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
The signal in MPI depends on magnetic nanoparticle (MNP) parameters and environmental conditions, as well as drive field (DF) settings and system-induced deviations. In this study, we propose a dictionary-based algorithm using a coupled Brown-Néel rotation model to simultaneously estimate the MNP parameters together with system transfer function. We then propose an empirical method that enables signal prediction at unmeasured DF frequencies.
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References
33:1904131, 2020, doi:10.1002/adma.201904131.
[2] A. Neumann, S. Draack, F. Ludwig, and T. M. Buzug, Parameter estimations of magnetic particles: A comparison between measurements
and simulations, in International Workshop on Magnetic Particle Imaging, 79, 2019.
[3] H. Albers, T. Kluth, and T. Knopp. Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of brown/néel dynamics and parameter identification problems in magnetic particle imaging. Journal of Magnetism and Magnetic Materials, 541:168508, 2022, doi:https://doi.org/10.1016/j.jmmm.2021.168508.
[4] A. Alpman, M. Utkur, and E. U. Saritas, MNP characterization and signal prediction using a model-based dictionary, in International
Workshop on Magnetic Particle Imaging, 8, 2022. doi:10.18416/IJMPI.2022.2203017.
[5] J. Weizenecker. The fokker–planck equation for coupled brown–néel-rotation. Physics in Medicine & Biology, 63(3):035004,
2018, doi:10.1088/1361-6560/aaa186.
[6] N.-S. Cheng. Formula for the viscosity of glycerol water mixture. Industrial Engineering Chemistry Research, 47(9):3285–3288, 2008, doi:10.1021/ie071349z.