MNP characterization and signal prediction using a model-based dictionary

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buir.contributor.authorAlpman, Asli
buir.contributor.authorUtkur, Mustafa
buir.contributor.authorSaritas, Emine Ulku
buir.contributor.orcidAlpman, Asli|0000-0003-1203-8295
buir.contributor.orcidUtkur, Mustafa |0000-0002-2521-9151
buir.contributor.orcidSaritas, Emine Ulku | 0000-0001-8551-1077
dc.citation.epage4en_US
dc.citation.issueNumber1 Suppl 1en_US
dc.citation.spage1en_US
dc.citation.volumeNumber8en_US
dc.contributor.authorAlpman, Asli
dc.contributor.authorUtkur, Mustafa
dc.contributor.authorSaritas, Emine Ulku
dc.date.accessioned2023-03-01T11:42:55Z
dc.date.available2023-03-01T11:42:55Z
dc.date.issued2022-03-21
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractMagnetic Particle Imaging (MPI) utilizes the nonlinear magnetic response of magnetic nanoparticles (MNPs) for signal localization. Accurate modeling of the magnetization behavior of MNPs is crucial for understanding their MPI signal responses. In this work, we propose a model-based dictionary approach using a coupled Brown-Néel rotation model. With experimental results on a Magnetic Particle Spectrometer (MPS), we show that this approach can successfully characterize MNPs and predict their signal responses.en_US
dc.identifier.doi10.18416/IJMPI.2022.2203017en_US
dc.identifier.urihttp://hdl.handle.net/11693/111996
dc.language.isoEnglishen_US
dc.publisherInfinite Science Publishingen_US
dc.relation.isversionofhttps://doi.org/10.18416/IJMPI.2022.2203017en_US
dc.source.titleInternational Journal on Magnetic Particle Imagingen_US
dc.titleMNP characterization and signal prediction using a model-based dictionaryen_US
dc.typeArticleen_US
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