Prediction of carotid hemodynamic descriptors based on ultrasound data and a neural network model

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Catarina Castro (Autor) FEUP; Carlos Alberto Conceição António (Autor) FEUP; Luisa Costa Sousa (Autor) FEUP

Indexação

Scopus - 0 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias

CORDIS: Ciências da Saúde ; Ciências Tecnológicas

Outras Informações

ID Authenticus: P-00G-21R

DOI: 10.1007/978-3-319-15799-3_12

Abstract (EN): The goal of this study was to analyse the hemodynamics in the carotid bifurcation and to evaluate its dependence on bifurcation geometry and presence of internal carotid artery (ICA) stenosis. Based on patient-specific ultrasound data, an optimal artificial neural network (ANN) model was developed searching data dimensional reduction. ANN estimated pulsatile conditions were used as boundary conditions along different points of the common carotid artery (CCA) and ICA for fluid dynamic simulations. Toward faster patient-specific hemodynamic and stenosis interpretation, ANN estimated blood flow descriptors were calculated and analysed. © Springer International Publishing Switzerland 2015.

Idioma: Inglês

Tipo (Avaliação Docente): Científica