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A soft-computing methodology for noninvasive time-spatial temperature estimation

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dc.contributor.authorTeixeira, C. A.
dc.contributor.authorRuano, M. Graça
dc.contributor.authorRuano, Antonio
dc.contributor.authorPereira, W. C. A.
dc.date.accessioned2013-02-07T12:52:19Z
dc.date.available2013-02-07T12:52:19Z
dc.date.issued2008
dc.date.updated2013-01-26T17:45:57Z
dc.description.abstractThe safe and effective application of thermal therapies is restricted due to lack of reliable noninvasive temperature estimators. In this paper, the temporal echo-shifts of backscattered ultrasound signals, collected from a gel-based phantom, were tracked and assigned with the past temperature values as radial basis functions neural networks input information. The phantom was heated using a piston-like therapeutic ultrasound transducer. The neural models were assigned to estimate the temperature at different intensities and points arranged across the therapeutic transducer radial line (60 mm apart from the transducer face). Model inputs, as well as the number of neurons were selected using the multiobjective genetic algorithm (MOGA). The best attained models present, in average, a maximum absolute error less than 0.5 C, which is pointed as the borderline between a reliable and an unreliable estimator in hyperthermia/diathermia. In order to test the spatial generalization capacity, the best models were tested using spatial points not yet assessed, and some of them presented a maximum absolute error inferior to 0.5 C, being “elected” as the best models. It should be also stressed that these best models present implementational low-complexity, as desired for real-time applications.por
dc.identifier.citationTeixeira, C. A.; Ruano, M. G.; Ruano, A. E.; Pereira, W. C. A. A Soft-Computing Methodology for Noninvasive Time-Spatial Temperature Estimation, IEEE Transactions on Biomedical Engineering, 55, 2, 572-580, 2008.por
dc.identifier.issn0018-9294
dc.identifier.otherAUT: MRU00118; ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2246
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIEEEpor
dc.subjectBiomedical acousticspor
dc.subjectFeedforward neural networkspor
dc.subjectGenetic algorithmspor
dc.subjectTemperature measurementpor
dc.titleA soft-computing methodology for noninvasive time-spatial temperature estimationpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage580por
oaire.citation.issue2por
oaire.citation.startPage572por
oaire.citation.titleIEEE Transactions on Biomedical Engineeringpor
oaire.citation.volume55por
person.familyNameTeixeira
person.familyNameRuano
person.familyNameRuano
person.familyNamePereira
person.givenNameCésar
person.givenNameMaria
person.givenNameAntonio
person.givenNameWagner
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0001-9396-1211
person.identifier.orcid0000-0002-0014-9257
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0001-5880-3242
person.identifier.ridA-3477-2012
person.identifier.ridA-8321-2011
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id55826531700
person.identifier.scopus-author-id7004483805
person.identifier.scopus-author-id7004284159
person.identifier.scopus-author-id35581987400
rcaap.rightsrestrictedAccesspor
rcaap.typearticlepor
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relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication5f0824cf-c471-4f03-8134-8003affbabe3
relation.isAuthorOfPublication.latestForDiscovery29e9844d-9355-4f2a-badf-9e7ad3117cdb

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