Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners

Detalhes bibliográficos
Autor(a) principal: Ribeiro, André Filipe dos Santos
Data de Publicação: 2012
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/9257
Resumo: Tese de mestrado em Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012
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spelling Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scannersTeses de mestrado - 2012Tese de mestrado em Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012Aim: Due to space and technical limitations in PET/MR scanners one of the difficulties is the generation of an attenuation correction (AC) map to correct the PET image data. Different methods have been suggested that make use of the images acquired with an ultrashort echo time (UTE) sequence. However, in most of them precise thresholds need to be defined and these may depend on the sequence parameters. In this thesis different algorithm based on artificial neural networks (ANN) are presented requiring little to any user interaction. Material and methods: An MR UTE sequence delivering two images with 0.07 ms and 2.46 ms echo times was acquired from a 3T MR-BrainPET for 9 patients. To correct for intensity inhomogeneities prior to attenuation map estimation a method based on multispetral images was developed and used to correct both images from UTE sequence. The training samples from the corrected images were feed to the proposed algorithms for learning and the methods posterior used for classification. The generated AC maps were compared to co-registered CT images based on the co-classification voxels, dice coefficients and sensitivity correction map (for the 9 patients), and relative differences (for 4 patients) in reconstructed PET images. Results: In overall the methods proposed showed high dice coefficients for air and soft tissue and lower to bone. Adittionaly, the proposed methods showed to present higher dice coefficients than remain methods. High linear correlation between the sensitivity correction maps was verified for all methods. The reconstructed PET images showed mean relative differences 5% for all methods except keereman method, where a mean of 6% was observed. Discussion: The different analysis showed slightly different results regarding the methods that perform best. Nevertheless, all the analysis showed that the methods developed work similar to better than the ones curently proposed. Conclusion: The methods aided by the template image showed to be more robust and with higher specificity than the ones without, altough loosing in sensitivity. Finally, the continuous methods developed showed to be promising as they can estimate different attenuation coefficients within a certain range for the same tissue and therefore account for different densities.Almeida, Pedro Miguel Dinis de, 1968-Kops, RotaRepositório da Universidade de LisboaRibeiro, André Filipe dos Santos2013-09-30T16:06:58Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/9257enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T15:53:31Zoai:repositorio.ul.pt:10451/9257Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:33:32.357518Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
title Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
spellingShingle Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
Ribeiro, André Filipe dos Santos
Teses de mestrado - 2012
title_short Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
title_full Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
title_fullStr Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
title_full_unstemmed Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
title_sort Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners
author Ribeiro, André Filipe dos Santos
author_facet Ribeiro, André Filipe dos Santos
author_role author
dc.contributor.none.fl_str_mv Almeida, Pedro Miguel Dinis de, 1968-
Kops, Rota
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Ribeiro, André Filipe dos Santos
dc.subject.por.fl_str_mv Teses de mestrado - 2012
topic Teses de mestrado - 2012
description Tese de mestrado em Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
2013-09-30T16:06:58Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/9257
url http://hdl.handle.net/10451/9257
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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