Fluid-Structure Interaction Analysis of Carotid Artery Blood Flow with Machine Learning Algorithm and OpenFOAM

Yıl 2022, Cilt: 26 Sayı: 6, 1131 - 1141, 31.12.2022

Murad Kucur , Banu Körbahti

https://doi.org/10.16984/saufenbilder.1173983

Öz

In this study, a patient-specific carotid artery model was analyzed with an open source program foam-extend. The research includes the effect of arterial wall deformation by fluid-structure analysis. Pulsatile velocity cycle is trained for 144 patients with different hemodynamic parameters, by machine learning algorithm using blood flow velocity measured from 337 points of the carotid artery. Data used for training is obtained from an open source in the literature. Here, the machine learning algorithm was created by the help of an open source code Phyton. Then, using trained values of machine learning, and the known systole and diastole blood pressures for a specific chosen patient, the patient-specific pulsatile velocity cycle was estimated. The estimated pulsatile velocity cycle was then fitted to Fourier series. This pulsatile velocity cycle is used as the input boundary condition for the model analyzed in foam-extend. The outlet boundary condition, pulsatile pressure cycle is found by 4-Element Windkessel algorithm. Wall shear stresses and time averaged wall shear stresses were obtained for both the rigid and fluid structure interaction models, and variation of displacement throughout the pulsatile cycle was found for the FSI model. Wall shear stresses, velocity, and displacements were obtained high at peak systole, consistent with pulsatile cycles. Like the wall shear stresses, the time averaged wall shear stresses for the FSI model were also found lower than the rigid model. The wall shear stresses showed an increase towards the exit of internal and external carotid artery.

Anahtar Kelimeler

Carotid artery, Machine learning, OpenFOAM, Blood flow

Kaynakça

• [1] P. Çeltikçi, Ö. Eraslan, M. A. Atıcı, I. Conkbayır, O. Ergun, H. Durmaz, E. Çeltikçi, “Application of machine learning algorithms for predicting internal carotid stenosis and comparing their value to duplex Doppler ultrasonography criteria,” Pamukkale Medical Journal, vol. 15, no. 2, pp. 213-222, 2022.
• [2] S. Savaş, N. Topaloğlu, Ö. Kazcı, P. N. Koşar, “Comparison of deep learning models in carotid artery Intima-Media thickness ultrasound images: CAIMTUSNet,” Bilişim Teknolojileri Dergisi, vol. 15, no. 1, pp. 1-11, 2022.
• [3] K. Perklot, M. Resch, H. Florian, “Pulsatile Non-Newtonian flow characteristics in a three-dimensional human carotid bifurcation model,” Journal of Biomechanical Engineering, Transactions of the ASME, vol. 113, pp. 464-475, 1991.
• [4] F. Yubo, J. Wentao, Z. Yuanwen, L Jinchuan, C. Junkai, D. Xiaoyan, “Numerical Simulation of Pulsatile Non-Newtonian Flow in the Carotid Artery Bifurcation,” Acta Mechanica Sinica, vol. 25, no. 249, pp. 249-255, 2009.
• [5] J. Moradicheghamahi, J. Sadeghiseraji, M. Jahangiri, “Numerical solution of the pulsatile, Non-Newtonian and turbulent flow in a patient specific elastic carotid artery,” International Journal of Mechanical Sciences, vol. 150, pp. 393-403, 2019.
• [6] N. Kumar, S. M. Abdul Khader, R. B. Pai, P. Kyriacou, S. Khan, K. Prakashini, R. Srikanth, “Effect of Newtonian and Non-Newtonian flow in subject specific carotid artery,” Journal of Engineering Science and Technology, vol. 14, no. 4, pp.2746-2763, 2020.
• [7] D. N. Ku, “Blood flow in arteries,” Annual Review of Fluid Mechanics, vol. 29, pp. 399-434, 1997.
• [8] B. K. Bharadvaj, R. F. Mabon, D. P. Giddens, “Steady flow in a model of the human carotid bifurcation part I- flow visualization,” Journal of Biomechanics, vol. 15, pp. 349-362, 1982.
• [9] D. Lopes, H. Puga, J. C. Teixeira, S. F. Teixeria, “Influence of arterial mechanical properties on carotid blood flow: comparison of CFD and FSI studies,” International Journal of Mechanical Sciences, vol. 160, pp. 209-218, 2019.
• [10] D. Lopes, H. Puga, J. C. Teixeira, S. F. Teixeria, “Fluid-Structure interaction study of carotid blood flow: Comparison between viscosity models,” European Journal of Mechanics/ B Fluids, vol. 83, pp. 226-234, 2020.
• [11] K. K. L. Wong, P. Thavornpattanapong, S. C. P. Cheung, J. Y. Tu, “Biomechanical investigation of pulsatile flow in a three-dimensional atherosclerotic carotid bifurcation model,” Journal of Mechanics in Medicine and Biology, vol. 13, pp.1-21, 2013.
• [12] S. Tada, M. Tarbell, “A Computational study flow in a compliant carotid bifurcation-stress phase angle correlation with shear stress,” Annals of Biomedical Engineering, vol. 33, no. 9, pp.1202-1212, 2005.
• [13] N. Kumar, S. M. Abdul Khader, R. Pai, S. H. Khan, P. A. Kyriacou, “Fluid structure interaction study of stenosed carotid artery considering the effects of blood pressure,” International Journal of Engineering Science, vol. 154, pp.1-14, 2020.
• [14] S. H. Lee, S. Kang, N. Hur., S. Jeong, “A Fluid-Structure interaction analysis on hemodynamics in carotid artery based on patient specific clinical data,” Journal of Mechanical Science and Technology, vol. 26, pp. 3821-3831, 2012.
• [15] Dr. M. Itagaki. (2019, September.30). The Biomedical 3D Printing Community, Available : http://www.embodi3d.com/ files /file/8523-head-and-neck-ct-dicom-dataset-for-teaching/.
• [16] B. Gschaider, H. Rusche, H. Jasak, H. Nilsson, M. Beaudoin, V. Skuric. (2013, December.30). Sourceforge, Available : http: //sourceforge.net/p/foamextend/wiki/Home.
• [17] P. H. Charlton, M. H. Jorge, V. Samuel, L. Ye, A.Jordi. (2019, April.10). Available : http://doi.org/10.528 /zenodo.3275625
• [18] P. H. Charlton, M. H. Jorge, V. Samuel, L. Ye, A. Jordi, “Modelling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 317, no. 5, pp. 1062-1085, 2019.
• [19] [19] L. Antiga, D. Steinman, S. Manini, R. Izzo. (2018, March.20). Vascular Modeling Toolkit, Available : http://www.vmtk.org.
• [20] [20] C. Cortes, V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.