Improvement of Sievert Integration Model in brachytherapy via inverse problems and Artificial Neural Networks

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dc.contributor.authorNASCIMENTO, ERIBERTO O. do
dc.contributor.authorOLIVEIRA, LUCAS N. de
dc.contributor.authorCALDAS, LINDA V.E.
dc.coverageInternacionalpt_BR
dc.date.accessioned2019-04-03T18:38:20Z
dc.date.available2019-04-03T18:38:20Z
dc.date.issued2019pt_BR
dc.description.abstractIncreasing the radial distance, the accuracy of the Sievert Integration Model (SIM) decreases in a nonlinear manner, adding errors up of 10% into the dose rate calculations; a similar fact occurs to the 2D anisotropy function where the errors may achieve 30% as already was related. For that reason, this paper sought an innovative approach to optimize the error variance and its biases of dose rate calculations around a Nucletron brachytherapy source of 192Ir from 0 to 10 cm taken in the radial distance, using an improved SIM through a hybrid coupling of Artificial Neural Networks (ANNs) and Inverse Problem Theory (IPT). Since the traditional approach relies into the use of a small data set of dose rate, the ANNs generalized these doses, making possible to search more broadly optimum parameters to SIM using the IPT. The results showed excellent accuracy evaluated with the Root Mean Square Percentage Error (RMSPE). In conclusion, the low RMSPE values indicate that the methodology is consistent, showing an excellent agreement with the state of art of dosimetric measurement techniques.pt_BR
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorshipIDCNPq: 165466/2015-4; 151013/2014-4; 301335/2016-8pt_BR
dc.format.extent260-264pt_BR
dc.identifier.citationNASCIMENTO, ERIBERTO O. do; OLIVEIRA, LUCAS N. de; CALDAS, LINDA V.E. Improvement of Sievert Integration Model in brachytherapy via inverse problems and Artificial Neural Networks. <b>Radiation Physics and Chemistry</b>, v. 155, p. 260-264, 2019. SI. DOI: <a href="https://dx.doi.org/10.1016/j.radphyschem.2018.05.024">10.1016/j.radphyschem.2018.05.024</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/29833.
dc.identifier.doi10.1016/j.radphyschem.2018.05.024pt_BR
dc.identifier.issn0969-806Xpt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-7362-2455
dc.identifier.percentilfi60.153pt_BR
dc.identifier.percentilfiCiteScore73.00
dc.identifier.suplementoSIpt_BR
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/29833
dc.identifier.vol155pt_BR
dc.relation.ispartofRadiation Physics and Chemistrypt_BR
dc.rightsopenAccesspt_BR
dc.sourceInternational Topical Meeting on Industrial Radiation and Radioisotope Measurement Applications, 10th, July 10-12, 2017, Chicago, IL, USApt_BR
dc.subjectbrachytherapy
dc.subjectsi units
dc.subjectneural networks
dc.subjectradiation doses
dc.subjectartificial intelligence
dc.subjectprogramming
dc.subjectinverse scattering problem
dc.titleImprovement of Sievert Integration Model in brachytherapy via inverse problems and Artificial Neural Networkspt_BR
dc.typeArtigo de periódicopt_BR
dspace.entity.typePublication
ipen.autorLINDA V. E. CALDAS
ipen.autorLUCAS NONATO DE OLIVEIRA
ipen.codigoautor1495
ipen.codigoautor10632
ipen.contributor.ipenauthorLINDA V. E. CALDAS
ipen.contributor.ipenauthorLUCAS NONATO DE OLIVEIRA
ipen.date.recebimento19-04pt_BR
ipen.identifier.fi2.226pt_BR
ipen.identifier.fiCiteScore3.7
ipen.identifier.ipendoc24801pt_BR
ipen.identifier.iwosWoSpt_BR
ipen.range.fi1.500 - 2.999
ipen.range.percentilfi50.00 - 74.99
ipen.type.genreArtigo
relation.isAuthorOfPublication7f46d4f4-dfd6-4485-a767-10df5b4f4f13
relation.isAuthorOfPublication87cc29f4-0e1f-4f7f-87ab-618b39834eb2
relation.isAuthorOfPublication.latestForDiscovery87cc29f4-0e1f-4f7f-87ab-618b39834eb2
sigepi.autor.atividadeOLIVEIRA, LUCAS N. DE:10632:330:Npt_BR
sigepi.autor.atividadeCALDAS, LINDA V.E.:1495:330:Npt_BR
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