Prediction of Carotid Hemodynamic Descriptors Based on Ultrasound Data and a Neural Network Model

[1]  Rosa Santos,et al.  Haemodynamic conditions of patient-specific carotid bifurcation based on ultrasound imaging , 2014, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[2]  João Manuel R. S. Tavares,et al.  Simulated hemodynamics in human carotid bifurcation based on Doppler ultrasound data , 2014 .

[3]  A. Marsden.  Optimization in Cardiovascular Modeling , 2014 .

[4]  João Manuel R. S. Tavares,et al.  A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images , 2013, Expert Syst. Appl..

[5]  Kiao Inthavong,et al.  Image-based computational hemodynamics evaluation of atherosclerotic carotid bifurcation models , 2013, Comput. Biol. Medicine.

[6]  Pascal Verdonck,et al.  A computational study of the hemodynamic impact of open- versus closed-cell stent design in carotid artery stenting. , 2013, Artificial organs.

[7]  J. Gillard,et al.  In vivo MRI-based simulation of fatigue process: a possible trigger for human carotid atherosclerotic plaque rupture , 2013, Biomedical engineering online.

[8]  Catarina F. Castro,et al.  Blood flow simulation and vascular reconstruction. , 2012, Journal of biomechanics.

[9]  Cornelius Weiller,et al.  In Vivo Wall Shear Stress Distribution in the Carotid Artery: Effect of Bifurcation Geometry, Internal Carotid Artery Stenosis, and Recanalization Therapy , 2010, Circulation. Cardiovascular imaging.

[10]  Jan Vierendeels,et al.  A simulation environment for validating ultrasonic blood flow and vessel wall imaging based on fluid-structure interaction simulations: ultrasonic assessment of arterial distension and wall shear rate. , 2010, Medical physics.

[11]  Matthieu De Beule,et al.  Patient-specific computational fluid dynamics: structured mesh generation from coronary angiography , 2010, Medical & Biological Engineering & Computing.

[12]  Dalin Tang,et al.  Sites of Rupture in Human Atherosclerotic Carotid Plaques Are Associated With High Structural Stresses: An In Vivo MRI-Based 3D Fluid-Structure Interaction Study , 2009, Stroke.

[13]  H. Torp,et al.  Ultrasound simulation of complex flow velocity fields based on computational fluid dynamics , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[14]  David A. Steinman,et al.  An image-based modeling framework for patient-specific computational hemodynamics , 2008, Medical & Biological Engineering & Computing.

[15]  P. Fischer,et al.  Direct numerical simulation of transitional flow in a stenosed carotid bifurcation. , 2008, Journal of biomechanics.

[16]  L. Antiga,et al.  Geometry of the Carotid Bifurcation Predicts Its Exposure to Disturbed Flow , 2008, Stroke.

[17]  E. Grossi.  The Framingham study and treatment guidelines for stroke prevention , 2008, Current treatment options in cardiovascular medicine.

[18]  Neil W Bressloff,et al.  Mining data from hemodynamic simulations via Bayesian emulation , 2007, Biomedical engineering online.

[19]  Sadik Kara,et al.  Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals , 2006, Expert Syst. Appl..

[20]  Elif Derya Übeyli,et al.  A recurrent neural network classifier for Doppler ultrasound blood flow signals , 2006, Pattern Recognit. Lett..

[21]  Sadik Kara,et al.  Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network , 2005, Expert Syst. Appl..

[22]  Elif Derya Übeyli,et al.  Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models , 2005, Comput. Biol. Medicine.

[23]  M. Kaazempur-Mofrad,et al.  Characterization of the Atherosclerotic Carotid Bifurcation Using MRI, Finite Element Modeling, and Histology , 2004, Annals of Biomedical Engineering.

[24]  Massimo Buscema,et al.  Recognition of patients with cardiovascular disease by artificial neural networks , 2004, Annals of medicine.

[25]  R Eugene Zierler,et al.  Carotid artery stenosis: gray-scale and Doppler US diagnosis--Society of Radiologists in Ultrasound Consensus Conference. , 2003, Radiology.

[26]  Carlos Alberto Conceição António,et al.  A multilevel genetic algorithm for optimization of geometrically nonlinear stiffened composite structures , 2002 .

[27]  P. Hoskins,et al.  Numerical investigation of physiologically realistic pulsatile flow through arterial stenosis. , 2001, Journal of biomechanics.

[28]  A. Brabazon,et al.  Assessment of degree of internal carotid artery stenosis based on duplex velocity measurements using an artificial neural network , 2001 .

[29]  H. Schumacher,et al.  Grading of internal carotid artery stenosis: validation of Doppler/duplex ultrasound criteria and angiography against endarterectomy specimen. , 2001, European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery.

[30]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[31]  B. H. Blott,et al.  Review of neural network applications in medical imaging and signal processing , 1992, Medical and Biological Engineering and Computing.

[32]  Catarina F. Castro,et al.  Blood Flow Simulation and Applications , 2012 .

[33]  João Manuel R. S. Tavares,et al.  Computer simulation of the carotid artery , 2012 .

[34]  Spyretta Golemati,et al.  Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks. , 2007, Ultrasound in medicine & biology.