In-Silico Analysis of Superparamagnetic Nanoparticles | Physics of the Contrast Agent in Magnetic Particle Imaging

Henrik Rogge

ISBN 10: 3945954134 / ISBN 13: 9783945954133

Editore: Infinite Science GmbH, 2015

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In-Silico Analysis of Superparamagnetic Nanoparticles | Physics of the Contrast Agent in Magnetic Particle Imaging | Henrik Rogge | Taschenbuch | 128 S. | Englisch | 2015 | Infinite Science GmbH | EAN 9783945954133 | Verantwortliche Person für die EU: Infinite Science GmbH, c/o MFC 1 - Technikzentrum, Maria-Goeppert-Str. 1, 23562 Lübeck, info[at]infinite-science[dot]de | Anbieter: preigu.

Codice articolo 104060679

Dati bibliografici

• Titolo: In-Silico Analysis of Superparamagnetic Nanoparticles | Physics of the Contrast Agent in Magnetic Particle Imaging
• Editore: Infinite Science GmbH
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3945954134
• ISBN 13: 9783945954133
• Rilegatura: Taschenbuch
• Condizione: Neu
• Cataloghi dei venditori:
  • Bücher

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Riassunto

Magnetic particle imaging (MPI) estimates the spatial distribution of super-paramagnetic iron-oxide nanoparticles (SPIOs) via the magnetization response of the particles subjected to static and ocillatory magnetic fields. This book focuses on the physics of the SPIOs with emphasis to the application in MPI and gives a brief introduction to the related particle physics. Stochastical Langevin equations are used to simulate the magnetization response. The stochastic differential equations, which incorporate the Néel and the Brown relaxation processes, are derived in detail and solved numerically. The solutions are validated with respect to the related Fokker-Planck equation. Subsequently, simulation studies are carried out to compare different particle parameters like the hydrodynamic diameter or the particle anisotropy with respect to the frequency of the magnetic excitation fields. In addition, a less complex particle model based on ordinary differential equations is presented to fit measurement data of a magnetic particle spectrometer. This optimization task is carried out by a new 2-dimensional continuous genetic algorithm.