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Article: Application of artifical neural network methods in HTS RF coil design for MRI

TitleApplication of artifical neural network methods in HTS RF coil design for MRI
Authors
KeywordsHigh-temperature superconducting (HTS)
RF coil;artificial neural network
Simulation
Electromagnetic
Issue Date2003
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.wiley.com/WileyCDA/WileyTitle/productCd-CMRB.html
Citation
Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering, 2003, v. 18B n. 1, p. 9-14 How to Cite?
AbstractThis article presents a new method of simulating high-temperature superconducting (HTS) RF coils using an electromagnetically trained artificial neural network (EM-ANN). This design is based on a spiral planar coil with distributed capacitance fabricated with Y1Ba2Cu3O7 (YBCO) films. Simulation time with this new method can be reduced to only one millionth of the time required by the commercial electromagnetic software programme HP Momentum. The new method can also exploit the properties of an artificial neural network by providing an inverse algorithm based on a resonant frequency input to derive other properties of an RF coil. This inverse algorithm using EM-ANN is easier, faster, and more interactive than the traditional “moment method.” The simulation results also show excellent agreement with experimental measurements, with a margin of error of less than 3%. © 2003 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 18B: 9–14, 2003
Persistent Identifierhttp://hdl.handle.net/10722/73694
ISSN
2015 Impact Factor: 0.762
2015 SCImago Journal Rankings: 0.278

 

DC FieldValueLanguage
dc.contributor.authorPang, H-
dc.contributor.authorShen, GG-
dc.date.accessioned2010-09-06T06:53:51Z-
dc.date.available2010-09-06T06:53:51Z-
dc.date.issued2003-
dc.identifier.citationConcepts in Magnetic Resonance Part B: Magnetic Resonance Engineering, 2003, v. 18B n. 1, p. 9-14-
dc.identifier.issn1552-5031-
dc.identifier.urihttp://hdl.handle.net/10722/73694-
dc.description.abstractThis article presents a new method of simulating high-temperature superconducting (HTS) RF coils using an electromagnetically trained artificial neural network (EM-ANN). This design is based on a spiral planar coil with distributed capacitance fabricated with Y1Ba2Cu3O7 (YBCO) films. Simulation time with this new method can be reduced to only one millionth of the time required by the commercial electromagnetic software programme HP Momentum. The new method can also exploit the properties of an artificial neural network by providing an inverse algorithm based on a resonant frequency input to derive other properties of an RF coil. This inverse algorithm using EM-ANN is easier, faster, and more interactive than the traditional “moment method.” The simulation results also show excellent agreement with experimental measurements, with a margin of error of less than 3%. © 2003 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 18B: 9–14, 2003-
dc.languageeng-
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.wiley.com/WileyCDA/WileyTitle/productCd-CMRB.html-
dc.relation.ispartofConcepts in Magnetic Resonance Part B: Magnetic Resonance Engineering-
dc.rightsConcepts in Magnetic Resonance Part B: Magnetic Resonance Engineering. Copyright © John Wiley & Sons, Inc.-
dc.rightsSpecial Statement for Preprint only Before publication: 'This is a preprint of an article accepted for publication in [The Journal of Pathology] Copyright © ([year]) ([Pathological Society of Great Britain and Ireland])'. After publication: the preprint notice should be amended to follows: 'This is a preprint of an article published in [include the complete citation information for the final version of the Contribution as published in the print edition of the Journal]' For Cochrane Library/ Cochrane Database of Systematic Reviews, add statement & acknowledgement : ‘This review is published as a Cochrane Review in the Cochrane Database of Systematic Reviews 20XX, Issue X. Cochrane Reviews are regularly updated as new evidence emerges and in response to comments and criticisms, and the Cochrane Database of Systematic Reviews should be consulted for the most recent version of the Review.’ Please include reference to the Review and hyperlink to the original version using the following format e.g. Authors. Title of Review. Cochrane Database of Systematic Reviews 20XX, Issue #. Art. No.: CD00XXXX. DOI: 10.1002/14651858.CD00XXXX (insert persistent link to the article by using the URL: http://dx.doi.org/10.1002/14651858.CD00XXXX) (This statement should refer to the most recent issue of the Cochrane Database of Systematic Reviews in which the Review published.)-
dc.subjectHigh-temperature superconducting (HTS)-
dc.subjectRF coil;artificial neural network-
dc.subjectSimulation-
dc.subjectElectromagnetic-
dc.titleApplication of artifical neural network methods in HTS RF coil design for MRI-
dc.typeArticle-
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1552-5031&volume=18B&issue=1&spage=9&epage=14&date=2003&atitle=Application+of+artifical+neural+network+methods+in+HTS+RF+coil+design+for+MRIen_HK
dc.identifier.emailShen, GG: gxshen@eee.hku.hk-
dc.identifier.authorityShen, GG=rp00166-
dc.identifier.doi10.1002/cmr.b.10076-
dc.identifier.hkuros82949-
dc.identifier.volume18B-
dc.identifier.issue1-
dc.identifier.spage9-
dc.identifier.epage14-
dc.publisher.placeUnited States-

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