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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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