Diffusion Microscopic Anisotropy Estimation in the brain
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/118696 |
Resumo: | Diffusion MRI (dMRI) is a non-invasive technique sensitive to microstructural changes that cannot be resolved by other conventional imaging techniques. Diffusion anisotropy measures from conventional dMRI techniques, such as Diffusion Tensor Imaging, do not only depend on tissue microstructural properties but are also confounded by tissue orientational dispersion. As an attempt to suppress this confounding effect, more advanced dMRI imaging techniques based on non-conventional diffusion MRI had been recently developed to quantify the microscopic diffusion fractional anisotropy (FA) without any prior assumptions of the underlying tissue. Measuring FA allows the assessment of microstructural alterations related to tissue maturation, degeneration, and pathology independently of changes in tissue organization. However, non-conventional dMRI sequences are not easily accessible on current clinical MRI scanners. Therefore, more recent studies had suggested the use of microstructural models to quantify FA from data acquired from conventional dMRI sequences. In this dissertation, the first reference open-source implementations of different microstructural models to estimate FA are provided, in which by computing the spherical mean of the signal acquired, the orientation’s influence from the dMRI data is withdrawn. These models included the one and two-compartmental spherical mean techniques (SMT1 and SMT2) and a novel adaption of the fiber ball imaging model for FA estimation. The implementation of these models is evaluated based on numerical simulations, tested on open-source in vivo data of a healthy human brain, and finally compared to the gold standard reference estimated from non-conventional dMRI sequences in a pre-clinical setting. Results show that though their parameter estimates are independent to tissue orientation effect, the SMT1 and SMT2 models provide over and underestimated values of FA, which were shown to be a consequence of their imposed assumptions. Although the adapted version of the FBI model did not show to provide robust FA estimates, its’ axonal water fraction estimates showed a high correlation to the gold standard FA estimates. This finding supports that axonal water fraction is a determinant factor of FA in heathy neural tissues. Therefore, in future studies, the further development of alternative techniques to estimate FA based on the information captured by FBI's axonal water estimates could be of interest. |
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Diffusion Microscopic Anisotropy Estimation in the braindiffusion MRIsingle diffusion encodingdouble diffusion encodingmicroscopic fractional anisotropymean techniquefiber ball imagingDomínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e TecnologiasDiffusion MRI (dMRI) is a non-invasive technique sensitive to microstructural changes that cannot be resolved by other conventional imaging techniques. Diffusion anisotropy measures from conventional dMRI techniques, such as Diffusion Tensor Imaging, do not only depend on tissue microstructural properties but are also confounded by tissue orientational dispersion. As an attempt to suppress this confounding effect, more advanced dMRI imaging techniques based on non-conventional diffusion MRI had been recently developed to quantify the microscopic diffusion fractional anisotropy (FA) without any prior assumptions of the underlying tissue. Measuring FA allows the assessment of microstructural alterations related to tissue maturation, degeneration, and pathology independently of changes in tissue organization. However, non-conventional dMRI sequences are not easily accessible on current clinical MRI scanners. Therefore, more recent studies had suggested the use of microstructural models to quantify FA from data acquired from conventional dMRI sequences. In this dissertation, the first reference open-source implementations of different microstructural models to estimate FA are provided, in which by computing the spherical mean of the signal acquired, the orientation’s influence from the dMRI data is withdrawn. These models included the one and two-compartmental spherical mean techniques (SMT1 and SMT2) and a novel adaption of the fiber ball imaging model for FA estimation. The implementation of these models is evaluated based on numerical simulations, tested on open-source in vivo data of a healthy human brain, and finally compared to the gold standard reference estimated from non-conventional dMRI sequences in a pre-clinical setting. Results show that though their parameter estimates are independent to tissue orientation effect, the SMT1 and SMT2 models provide over and underestimated values of FA, which were shown to be a consequence of their imposed assumptions. Although the adapted version of the FBI model did not show to provide robust FA estimates, its’ axonal water fraction estimates showed a high correlation to the gold standard FA estimates. This finding supports that axonal water fraction is a determinant factor of FA in heathy neural tissues. Therefore, in future studies, the further development of alternative techniques to estimate FA based on the information captured by FBI's axonal water estimates could be of interest.A ressonância magnética por difusão (dMRI, do inglês diffusion magnetic resonance imaging) é uma técnica não invasiva, sensível a alterações microestruturais que podem não ser resolvidas por outras técnicas de imagem estruturais convencionais. As medidas de anisotropia, a partir de técnicas convencionais de dMRI, não dependem apenas das propriedades microestruturais do tecido, mas também da dispersão na orientação do tecido. Na tentativa de suprimir esse efeito, foram recentemente desenvolvidas técnicas mais avançadas de imagem de ressonância magnética por difusão baseadas em sequências de difusão não convencionais de forma a quantificar a anisotropia microscópica fracionária de difusão (FA, do inglês microscopic fractional anisotropy) sem qualquer suposição prévia sobre o tecido subjacente. A medição da anisotropia microscópica de difusão permite a avaliação das alterações microestruturais relacionadas à maturação, degeneração e patologia de um tecido, independentemente das alterações na organização do mesmo. No entanto, as sequências de dMRI não convencionais não são facilmente acessíveis nos aparelhos de ressonância magnética atuais. Posto isto, estudos mais recentes sugeriram o uso de modelos microestruturais para quantificar FA a partir de dados adquiridos através de sequências de dMRI convencionais. No decorrer desta dissertação, são fornecidas as primeiras implementações de diferentes modelos microestruturais de referência em open-source para estimar FA. Modelos estes que através do cálculo da média esférica de um sinal adquirido, retiram a influência da orientação nos dados de dMRI. Esses modelos incluem as técnicas baseadas em médias esféricas de um sinal dMRI (SMT, do inglês Spherical Mean Techniques), de um e dois compartimentos, (SMT1 e SMT2) e uma nova adaptação do modelo da imagem por fibras em bola (FBI, do inglês Fiber Ball Imaging) para estimar o FA. A implementação dos modelos é avaliada tendo como base simulações numéricas, testadas em dados de cérebros humanos saudáveis, adquiridos in vivo. Finalmente, o FA é então comparado com a referência padrão estimada a partir de sequências dMRI não convencionais, num ambiente pré-clínico. Os resultados mostram que, embora as estimativas dos parâmetros sejam independentes do efeito da orientação do tecido, os modelos SMT1 e SMT2 fornecem valores de FA acima e abaixo da referência, o que se mostra ser uma consequência das suposições impostas. Embora a versão adaptada do modelo FBI não tenha resultado em estimativas robustas de FA, as suas estimativas da fração de água axonal (AWF, do inglês axonal water fraction) evidenciaram uma alta correlação com as estimativas padrão de FA. Assim, estes resultados demonstram que a AWF é, efetivamente, um fator determinante de FA em tecidos neuronais saudáveis. No que concerne a estudos futuros, pode revelar-se interessante analisar o desenvolvimento de técnicas alternativas para estimar FA com base em informações obtidas pelas estimativas de AWF do modelo FBI.Henriques, RafaelVigário, RicardoRUNSá, Simão Bolota de Couto2021-06-02T08:50:00Z2021-0220202021-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/118696enginfo: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:RCAAP2024-03-11T05:01:31Zoai:run.unl.pt:10362/118696Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:56.460025Repositório 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 |
Diffusion Microscopic Anisotropy Estimation in the brain |
title |
Diffusion Microscopic Anisotropy Estimation in the brain |
spellingShingle |
Diffusion Microscopic Anisotropy Estimation in the brain Sá, Simão Bolota de Couto diffusion MRI single diffusion encoding double diffusion encoding microscopic fractional anisotropy mean technique fiber ball imaging Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
title_short |
Diffusion Microscopic Anisotropy Estimation in the brain |
title_full |
Diffusion Microscopic Anisotropy Estimation in the brain |
title_fullStr |
Diffusion Microscopic Anisotropy Estimation in the brain |
title_full_unstemmed |
Diffusion Microscopic Anisotropy Estimation in the brain |
title_sort |
Diffusion Microscopic Anisotropy Estimation in the brain |
author |
Sá, Simão Bolota de Couto |
author_facet |
Sá, Simão Bolota de Couto |
author_role |
author |
dc.contributor.none.fl_str_mv |
Henriques, Rafael Vigário, Ricardo RUN |
dc.contributor.author.fl_str_mv |
Sá, Simão Bolota de Couto |
dc.subject.por.fl_str_mv |
diffusion MRI single diffusion encoding double diffusion encoding microscopic fractional anisotropy mean technique fiber ball imaging Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
topic |
diffusion MRI single diffusion encoding double diffusion encoding microscopic fractional anisotropy mean technique fiber ball imaging Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
description |
Diffusion MRI (dMRI) is a non-invasive technique sensitive to microstructural changes that cannot be resolved by other conventional imaging techniques. Diffusion anisotropy measures from conventional dMRI techniques, such as Diffusion Tensor Imaging, do not only depend on tissue microstructural properties but are also confounded by tissue orientational dispersion. As an attempt to suppress this confounding effect, more advanced dMRI imaging techniques based on non-conventional diffusion MRI had been recently developed to quantify the microscopic diffusion fractional anisotropy (FA) without any prior assumptions of the underlying tissue. Measuring FA allows the assessment of microstructural alterations related to tissue maturation, degeneration, and pathology independently of changes in tissue organization. However, non-conventional dMRI sequences are not easily accessible on current clinical MRI scanners. Therefore, more recent studies had suggested the use of microstructural models to quantify FA from data acquired from conventional dMRI sequences. In this dissertation, the first reference open-source implementations of different microstructural models to estimate FA are provided, in which by computing the spherical mean of the signal acquired, the orientation’s influence from the dMRI data is withdrawn. These models included the one and two-compartmental spherical mean techniques (SMT1 and SMT2) and a novel adaption of the fiber ball imaging model for FA estimation. The implementation of these models is evaluated based on numerical simulations, tested on open-source in vivo data of a healthy human brain, and finally compared to the gold standard reference estimated from non-conventional dMRI sequences in a pre-clinical setting. Results show that though their parameter estimates are independent to tissue orientation effect, the SMT1 and SMT2 models provide over and underestimated values of FA, which were shown to be a consequence of their imposed assumptions. Although the adapted version of the FBI model did not show to provide robust FA estimates, its’ axonal water fraction estimates showed a high correlation to the gold standard FA estimates. This finding supports that axonal water fraction is a determinant factor of FA in heathy neural tissues. Therefore, in future studies, the further development of alternative techniques to estimate FA based on the information captured by FBI's axonal water estimates could be of interest. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2021-06-02T08:50:00Z 2021-02 2021-02-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/118696 |
url |
http://hdl.handle.net/10362/118696 |
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eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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