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

• Type: Article in International Scientific Journal
• Date: 2015

Authors

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

Indexing

Scopus - 0 Citations

Scientific classification

FOS: Engineering and technology

CORDIS: Health sciences ; Technological sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific