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, "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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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	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 doi:10.4203/ccp.108.284 purchase the full-text of this paper 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. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
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