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Field synthesis for the optimal treatment planning in Magnetic Fluid Hyperthermia

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An automated procedure based on evolutionary computation and Finite Element Analysis (FEA) is proposed to synthesize the optimal distribution of nanoparticles (NPs) in multi-site injection for a Magnetic Fluid Hyperthermia (MFH) therapy. Evolution Strategy and Non dominated Sorting Genetic Algorithm (NSGA) are used as optimization procedures coupled with a Finite Element computation tool.
Rocznik
Strony
57--67
Opis fizyczny
Bibliogr. 24 poz., tab., rys.
Twórcy
autor
autor
autor
Bibliografia
  • 1. Goya G.F., Grazú V., Ibarra M.R., Magnetic Nanoparticles for Cancer Therapy. Curr. Nanosc. 4: 1-16 (2008).
  • 2. Rosensweig R.E., Heating magnetic fluid with alternating magnetic field. J. Magn. Magn. Mat., pp. 370-374 (2002).
  • 3. Gneveckow U., Jordan A., Scholz Volker Brüß R. et al., Description and characterization of the novel hyperthermia and thermoablation-system MFH®300F for clinical magnetic fluid hyperthermia. Med. Phys. 31(6): 1444-1451 (2004).
  • 4. Di Barba P., Dughiero F., Sieni E., Magnetic field synthesis in the design of inductors for magnetic fluid hyperthermia. IEEE Trans on Magn. 46: 2931-2934 (2010).
  • 5. Di Barba P., Dughiero F., Trevisan F., Optimization of the Loney’s solenoid through Quasi-analytical strategies,: a benchmark problem reconsidered. IEEE Trans. Magn. 33: 1864-1867 (1997).
  • 6. Moroz P., Jones S.K., Gray B.N., Magnetically mediated hyperthermia: current status and future directions. Int. J. Hyperthermia 18(4): 267-284 (2002).
  • 7. Curley S.A., New Approaches to the Treatment of Hepatic Malignancies. Radiofrequency Ablation of Malignant Liver Tumors. Annals of Surgical Oncology 10(4): 338-347 (2003).
  • 8. Candeo A., Dughiero F., Numerical FEM models for the planning of magnetic induction hyperthermia treatments with nanoparticles. IEEE Trans. Magn. 45: 1654-1657 (2009).
  • 9. Salloum M., Ma R., Zhu L., Enhancements in treatment planning for magnetic nanoparticle hyperthermia: optimization of the heat absorption pattern. Int. J. of Hyperthermia 25 (2009).
  • 10. M. Salloum, R. Ma, L. Zhu, An in-vivo experimental study of temperature elevations in animal tissue during magnetic nanoparticle hyperthermia. Int. J. Hyperth. 24: 589-601 (2008).
  • 11. Di Barba P., Dughiero F., Sieni E., Candeo E.A., Coupled Field Synthesis in Magnetic Fluid Hyperthermia. Magnetics, IEEE Transactions on 47(5): 914-917 (2010).
  • 12. Di Barba P., Multiobjective Shape Design in Electricity and Magnetism. Springer (2010).
  • 13. Di Barba P., Palka R., Optimization of the HTSC-PM Interaction in Magnetic Bearings by a Multiobjective Design. Proc. Int Symp. Electromagnetic Fields in Mechatronics, Electrical and Electronic Eng. pp. 94-95 (2007).
  • 14. Di Barba P., Mognaschi M.E., Palka R., Savini A., Optimization of the MIT Field Exciter by a Multiobjective Design. IEEE Trans. Magn. 45(3): 1530-1533 (2009).
  • 15. Binns K.J., Lawrenson P.J., Trowbridge C.W., The Analytical and Numerical Solution of Electric and Magnetic Fields. Wiley & Sons Ltd, Chichester (1992).
  • 16. Carslaw H.S., Jaeger J.C., Conduction of heat in solids. Clarendon Press, Oxford (1959).
  • 17. Pennes H.H., Analysis of tissue and arterial blood temperatures in the resting human forearm. J. Appl. Physiol. 85: 5-34 (1948).
  • 18. Preston T.W., Reece A.B.J., Solution of 3-Dimensional eddy current problems: the T-Ω method. IEEE Trans. Magn. 18: 486-491 (1982).
  • 19. Biro O., Preis K., Vrisk G., Richter K.R., Ticar I., Computation of 3-D magnetostatic fields using a reduced scalar potential. IEEE Trans. Magn. 29: 1329-1332 (1993).
  • 20. www.cedrat.com (last visited January 2012).
  • 21.] Lang J., Erdmann B., Seebass M., Impact of nonlinear heat transfer on temperature control in regional hyperthermia. IEEE Trans. Biom. Eng. 46: 1129-1138 (1999).
  • 22. Di Barba P., Dughiero F., Sieni E., Synthesizing Distributions of Magnetic Nanoparticles for Clinical Hyperthermia. IEEE Trans. Magn., in press.
  • 23. Deb K., Pratap A., Agarwal S., Meyarivan E.T., A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on 6(2): 182-197 (2002).
  • 24. Hudy W., Jaracz K., Selection of control parameters in a control system with a DC electric series motor using evolutionary algorithm. Archives of Electrical Engineering 60(3): 231-237 (2011).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS4-0001-0034
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