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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 108
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING
Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping
Paper 284

Integrating Ultrasound Images using Modular Artificial Neural Networks

C.F. Castro, C.A. Conceição António and L.C. Sousa

INEGI and DEMec, Faculty of Engineering, University of Porto, Portugal

Full Bibliographic Reference for this paper
, "Integrating Ultrasound Images using Modular Artificial Neural Networks", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 284, 2015. doi:10.4203/ccp.108.284
Keywords: artificial neural network, modular simulation model, carotid bifurcation, Doppler image based analysis.

Summary
The aim of the work, described in this paper, was to develop a new set-up to infer on significant disturbances of carotid artery hemodynamics based on the analysis of clinical Doppler acquisitions. A patient-specific numerical simulation system for the analysis of arterial blood flow under pulsatile conditions using a modular artificial neural network with optimal configuration was implemented based on carotid geometry and Doppler hemodynamic features. With arterial blood being addressed as a time series dependent on heart pulse periodicity, splitting parameters given by carotid bifurcation branches were able to allocate the input-output vectors for different flow regimes. Predictions given by the optimal modular artificial neural network model were able to reproduce patient-specific velocity patterns. The hemodynamic behaviour of healthy, low grade and high grade carotid artery stenosis was addressed suggesting further developments for hemodynamic classification.

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