RESUMO
New complex tissue microstructure estimators have been presented recently in order to elucidate white matter fibre orientations. Since these algorithms are based on the diffusion-weighted signal profile, the estimations are affected by noise artefacts. The proven robustness of these methods cannot counteract distortions since the statistical Rician behavior has not been taken into account. In this study, two techniques to counteract the noise distortions are presented to improve the fibre orientation estimations. Simulations and in vivo experiments show an improvement in the angular resolution and convergence of the results. One of these strategies represents a good compromise between computational cost and result improvements.
Assuntos
Artefatos , Encéfalo/anatomia & histologia , Imagem de Difusão por Ressonância Magnética/métodos , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Fibras Nervosas Mielinizadas/ultraestrutura , Reconhecimento Automatizado de Padrão/métodos , Algoritmos , Inteligência Artificial , Interpretação Estatística de Dados , Humanos , Imageamento Tridimensional/métodos , Modelos Anatômicos , Modelos Neurológicos , Modelos Estatísticos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
A deconvolution approach is presented to solve fiber crossing in diffusion magnetic resonance imaging. In order to provide a direct physical interpretation of the signal generation process, we started from the classical multicompartment model and rewrote this in terms of a convolution process, identifying a significant scalar parameter alpha to characterize the physical system response. Deconvolution is performed by a modified version of the Richardson-Lucy algorithm. Simulations show the ability of this method to correctly separate fiber crossing, even in the presence of noisy data, with lower signal-to-noise ratio, and imprecision in the impulse response function imposed during deconvolution. The in vivo data confirms the efficacy of this method to resolve fiber crossing in real complex brain structures. These results suggest the usefulness of our approach in fiber tracking or connectivity studies.
