Diffusion Microscopic Anisotropy Estimation in the brain

• Autor(a) principal: Sá, Simão Bolota de Couto
• 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
• 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

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in which by computing the spherical mean of the signal acquired, the orientation’s influence from the dMRI data is withdrawn.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.