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1.
MAGMA ; 33(3): 357-365, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31722036

RESUMO

OBJECTIVE: Cerebral blood flow (CBF) quantification using dynamic-susceptibility contrast MRI can be achieved via model-independent deconvolution, with local arterial input function (AIF) deconvolution methods identifying multiple arterial regions with unique corresponding arterial input functions. The clinical application of local AIF methods necessitates an efficient and fully automated solution. To date, such local AIF methods have relied on the computation of a singular surrogate measure of bolus arrival time or custom arterial scoring functions to infer vascular supply origins. This paper aims to introduce a new local AIF method that alternatively utilises a multi-stage approach to perform AIF selection. MATERIAL AND METHODS: A fully automated, multi-stage local AIF method is proposed, leveraging both signal-based cluster analysis and priority flooding to define arterial regions and their corresponding vascular supply origins. The introduced method was applied to data from four patients with cerebrovascular disease who showed significant artefacts when using a prevailing automated local AIF method. RESULTS: The immediately apparent image artefacts found using the pre-existing method due to poor AIF selection were found to be absent when using the proposed method. CONCLUSION: The results suggest the proposed solution provides a more robust approach to perfusion quantification than currently available fully automated local AIF methods.


Assuntos
Circulação Cerebrovascular , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Reconhecimento Automatizado de Padrão , Algoritmos , Artérias , Artefatos , Automação , Encéfalo/diagnóstico por imagem , Transtornos Cerebrovasculares/diagnóstico por imagem , Análise por Conglomerados , Meios de Contraste , Humanos , Doença de Moyamoya/diagnóstico por imagem , Distribuição Normal , Perfusão
2.
Neuroimage ; 202: 116137, 2019 11 15.
Artigo em Inglês | MEDLINE | ID: mdl-31473352

RESUMO

MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.


Assuntos
Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Neuroimagem , Design de Software , Imagem de Difusão por Ressonância Magnética , Humanos
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