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On the possibility of non-invasive multilayer temperature estimation using soft-computing methods

2010-01-01Journal article Restricted access
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dc.contributor.authorTeixeira, C. A.
dc.contributor.authorPereira, W. C. A.
dc.contributor.authorRuano, Antonio
dc.contributor.authorRuano, M. Graça
dc.date.accessioned2015-04-13T14:53:13Z
dc.date.available2015-04-13T14:53:13Z
dc.date.issued2010-01-01
dc.description.abstractObjective and motivation: This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics (e. g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. Novelty aspects: The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. Materials and methods: In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic algorithm (MOGA). In this work 40 operating conditions were considered, i.e. five 5-mm spaced spatial points and eight therapeutic intensities (I(SATA)): 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7 and 2:0 W/cm(2). Models were trained and selected to estimate temperature at only four intensities, then during the validation phase, the best-fitted models were analyzed in data collected at the eight intensities. This procedure leads to a more realistic evaluation of the generalisation level of the best-obtained structures. Results and discussion: At the end of the identification phase, 82 (preferable) estimator models were achieved. The majority of them present an average maximum absolute error (MAE) inferior to 0.5 degrees C. The best-fitted estimator presents a MAE of only 0.4 degrees C for both the 40 operating conditions. This means that the gold-standard maximum error (0.5 degrees C) pointed for hyperthermia was fulfilled independently of the intensity and spatial position considered, showing the improved generalisation capacity of the identified estimator models. As the majority of the preferable estimator models, the best one presents 6 inputs and 11 neurons. In addition to the appropriate error performance, the estimator models present also a reduced computational complexity and then the possibility to be applied in real-time. Conclusions: A non-invasive temperature estimation model, based on soft-computing technique, was proposed for a three-layered phantom. The best-achieved estimator models presented an appropriate error performance regardless of the spatial point considered (inside or at the interface of the layers) and of the intensity applied. Other methodologies published so far, estimate temperature only in homogeneous media. The main drawback of the proposed methodology is the necessity of a-priory knowledge of the temperature behavior. Data used for training and optimisation should be representative, i.e., they should cover all possible physical situations of the estimation environment. (C) 2009 Elsevier B.V. All rights reserved.
dc.identifier.doihttp://dx.doi.org/10.1016/j.ultras.2009.07.005
dc.identifier.issn0041-624X
dc.identifier.otherAUT: MRU00118; ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/5864
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.subjectNon-invasive temperature estimation
dc.subjectUltrasound therapy
dc.subjectMultilayered media
dc.subjectArtificial neural networks
dc.subjectSoft-computing methods
dc.subject.otherBackscattered ultrasound
dc.subject.otherNeural-networks
dc.subject.otherPrediction
dc.subject.otherAlgorithm
dc.subject.otherEnergy
dc.titleOn the possibility of non-invasive multilayer temperature estimation using soft-computing methods
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage43
oaire.citation.issue1
oaire.citation.startPage32
oaire.citation.titleUltrasonics
oaire.citation.volume50
person.familyNameTeixeira
person.familyNamePereira
person.familyNameRuano
person.familyNameRuano
person.givenNameCésar
person.givenNameWagner
person.givenNameAntonio
person.givenNameMaria
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0001-9396-1211
person.identifier.orcid0000-0001-5880-3242
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0002-0014-9257
person.identifier.ridA-3477-2012
person.identifier.ridB-4135-2008
person.identifier.ridA-8321-2011
person.identifier.scopus-author-id55826531700
person.identifier.scopus-author-id35581987400
person.identifier.scopus-author-id7004284159
person.identifier.scopus-author-id7004483805
rcaap.rightsrestrictedAccess
rcaap.typearticle
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relation.isAuthorOfPublication5f0824cf-c471-4f03-8134-8003affbabe3
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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