Research Papers

Numerical Study of Cerebroarterial Hemodynamic Changes Following Carotid Artery Operation: A Comparison Between Multiscale Modeling and Stand-Alone Three-Dimensional Modeling

[+] Author and Article Information
Fuyou Liang

SJTU-CU International Cooperative
Research Center,
School of Naval Architecture,
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Shanghai 200240, China
e-mail: fuyouliang@sjtu.edu.cn

Marie Oshima

Institute of Industrial Science,
The University of Tokyo,
Tokyo 153-8505, Japan

Huaxiong Huang

Department of Mathematics and Statistics,
York University,
Toronto, ON M3J 1P3 Canada

Hao Liu

SJTU-CU International Cooperative
Research Center,
School of Naval Architecture,
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Shanghai 200240, China;
Graduate School of Engineering,
Chiba University,
Chiba-Shi, Chiba 263-8522, Japan

Shu Takagi

Department of Mechanical Engineering,
The University of Tokyo,
Tokyo 113-8654, Japan

1Corresponding author.

Manuscript received May 1, 2015; final manuscript received August 14, 2015; published online September 7, 2015. Assoc. Editor: Tim David.

J Biomech Eng 137(10), 101011 (Sep 07, 2015) (12 pages) Paper No: BIO-15-1211; doi: 10.1115/1.4031457 History: Received May 01, 2015; Revised August 14, 2015

Free outflow boundary conditions have been widely adopted in hemodynamic model studies, they, however, intrinsically lack the ability to account for the regulatory mechanisms of systemic hemodynamics and hence carry a risk of producing incorrect results when applied to vascular segments with multiple outlets. In the present study, we developed a multiscale model capable of incorporating global cardiovascular properties into the simulation of blood flows in local vascular segments. The multiscale model was constructed by coupling a three-dimensional (3D) model of local arterial segments with a zero-one-dimensional (0-1-D) model of the cardiovascular system. Numerical validation based on an idealized model demonstrated the ability of the multiscale model to preserve reasonable pressure/flow wave transmission among different models. The multiscale model was further calibrated with clinical data to simulate cerebroarterial hemodynamics in a patient undergoing carotid artery operation. The results showed pronounced hemodynamic changes in the cerebral circulation following the operation. Additional numerical experiments revealed that a stand-alone 3D model with free outflow conditions failed to reproduce the results obtained by the multiscale model. These results demonstrated the potential advantage of multiscale modeling over single-scale modeling in patient-specific hemodynamic studies. Due to the fact that the present study was limited to a single patient, studies on more patients would be required to further confirm the findings.

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Xiang, J. , Tutino, V. M. , Snyder, K. V. , and Meng, H. , 2014, “ CFD: Computational Fluid Dynamics or Confounding Factor Dissemination? The Role of Hemodynamics in Intracranial Aneurysm Rupture Risk Assessment,” Am. J. Neuroradiology, 35(10), pp. 1849–1857. [CrossRef]
Van Ooij, P. , Guédon, A. , Poelma, C. , Schneiders, J. , Rutten, M. C. M. , Marquering, H. A. , and Nederveen, A. J. , 2012, “ Complex Flow Patterns in a Real-Size Intracranial Aneurysm Phantom: Phase Contrast MRI Compared With Particle Image Velocimetry and Computational Fluid Dynamics,” NMR Biomed., 25(1), pp. 14–26. [CrossRef] [PubMed]
Mynard, J. P. , Wasserman, B. A. , and Steinman, D. A. , 2013, “ Errors in the Estimation of Wall Shear Stress by Maximum Doppler Velocity,” Atherosclerosis, 227(2), pp. 259–266. [CrossRef] [PubMed]
Castro, M. , Putman, C. , Radaelli, A. , Frangi, A. , and Cebral, J. , 2009, “ Hemodynamics and Rupture of Terminal Cerebral Aneurysms,” Acad. Radiol., 16(10), pp. 1201–1207. [CrossRef] [PubMed]
Chong, W. , Zhang, Y. , Qian, Y. , Lai, L. , Parker, G. , and Mitchell, K. , 2014, “ Computational Hemodynamics Analysis of Intracranial Aneurysms Treated With Flow Diverters: Correlation With Clinical Outcomes,” Am. J. Neuroradiology, 35(1), pp. 136–142. [CrossRef]
Moore, S. , David, T. , Chase, J. G. , Arnold, J. , and Fink, J. , 2006, “ 3D Models of Blood Flow in the Cerebral Vasculature,” J. Biomech., 39(8), pp. 1454–1463. [CrossRef] [PubMed]
Geers, A. J. , Larrabide, I. , Morales, H. G. , and Frangi, A. F. , 2014, “ Approximating Hemodynamics of Cerebral Aneurysms With Steady Flow Simulations,” J. Biomech., 47(1), pp. 178–185. [CrossRef] [PubMed]
Morales, H. G. , and Bonnefous, O. , 2014, “ Peak Systolic or Maximum Intra-Aneurysmal Hemodynamic Condition? Implications on Normalized Flow Variables,” J. Biomech., 47(10), pp. 2362–2370. [CrossRef] [PubMed]
Morbiducci, U. , Gallo, D. , Massai, D. , Consolo, F. , Ponzini, R. , Antiga, L. , and Redaelli, A. , 2010, “ Outflow Conditions for Image-Based Hemodynamic Models of the Carotid Bifurcation: Implications for Indicators of Abnormal Flow,” ASME J. Biomech. Eng., 132(9), p. 091005. [CrossRef]
Morbiducci, U. , Gallo, D. , Ponzini, R. , Massai, D. , Antiga, L. , Montevecchi, F. M. , and Redaelli, A. , 2010, “ Quantitative Analysis of Bulk Flow in Image-Based Hemodynamic Models of the Carotid Bifurcation: The Influence of Outflow Conditions as Test Case,” Ann. Biomed. Eng., 38(12), pp. 3688–3705. [CrossRef] [PubMed]
Alastruey, J. , Moore, S. M. , Parker, K. H. , David, T. , Peiró, J. , and Sherwin, S. J. , 2008, “ Reduced Modelling of Blood Flow in the Cerebral Circulation: Coupling 1-D, 0-D and Cerebral Auto-Regulation Models,” Int. J. Numer. Methods Fluids, 56(8), pp. 1061–1067. [CrossRef]
Appanaboyina, S. , Mut, F. , Löhner, R. , Scrivano, E. , Miranda, C. , Lylyk, P. , and Cebral, J. , 2008, “ Computational Modelling of Blood Flow in Side Arterial Branches After Stenting of Cerebral Aneurysms,” Int. J. Comput. Fluid Dyn., 22(10), pp. 669–676. [CrossRef]
Dong, J. , Wong, K. K. , and Tu, J. , 2013, “ Hemodynamics Analysis of Patient-Specific Carotid Bifurcation: A CFD Model of Downstream Peripheral Vascular Impedance,” Int. J. Numer. Methods Biomed. Eng., 29(4), pp. 476–491. [CrossRef]
Huang, P. G. , and Muller, L. O. , 2015, “ Simulation of One-Dimensional Blood Flow in Networks of Human Vessels Using a Novel TVD Scheme,” Int. J. Numer. Methods Biomed. Eng. 31(5):e02701.
Liang, F. Y. , Fukasaku, K. , Liu, H. , and Takagi, S. , 2011, “ A Computational Model Study of the Influence of the Anatomy of the Circle of Willis on Cerebral Hyperperfusion Following Carotid Artery Surgery,” Biomed. Eng. Online, 10(84), pp. 1–22. [PubMed]
Alastruey, J. , Parker, K. H. , Peiro, J. , Byrd, S. M. , and Sherwin, S. J. , 2007, “ Modelling the Circle of Willis to Assess the Effects of Anatomical Variations and Occlusions on Cerebral Flows,” J. Biomech., 40(8), pp. 1794–1805. [CrossRef] [PubMed]
Grinberg, L. , Cheever, E. , Anor, T. , Madsen, J. R. , and Karniadakis, G. E. , 2011, “ Modeling Blood Flow Circulation in Intracranial Arterial Networks: A Comparative 3D/1D Simulation Study,” Ann. Biomed. Eng., 39(1), pp. 297–309. [CrossRef] [PubMed]
Esmaily Moghadam, M. , Vignon-Clementel, I . E. , Figliola, R. , and Marsden, A. L. , 2013, “ A Modular Numerical Method for Implicit 0D/3D Coupling in Cardiovascular Finite Element Simulations,” J. Comput. Phys., 244, pp. 63–79. [CrossRef]
Formaggia, L. , Quarteroni, A. , and Vergara, C. , 2013, “ On the Physical Consistency Between Three-Dimensional and One-Dimensional Models in Haemodynamics,” J. Comput. Phys., 244, pp. 97–112. [CrossRef]
Passerini, T. , De Luca, M. , Formaggia, L. , Quarteroni, A. , and Veneziani, A. , 2009, “ A 3D/1D Geometrical Multiscale Model of Cerebral Vasculature,” J. Eng. Math., 64(4), pp. 319–330. [CrossRef]
Pennati, G. , Corsini, C. , Cosentino, D. , Hsia, T. Y. , Luisi, V. S. , Dubini, G. , and Migliavacca, F. , 2011, “ Boundary Conditions of Patient-Specific Fluid Dynamics Modelling of Cavopulmonary Connections: Possible Adaptation of Pulmonary Resistances Results in a Critical Issue for a Virtual Surgical Planning,” Interface Focus, 1(3), pp. 297–307. [CrossRef] [PubMed]
Blanco, P. J. , and Feijóo, R. A. , 2013, “ A Dimensionally-Heterogeneous Closed-Loop Model for the Cardiovascular System and Its Applications,” Med. Eng. Phys., 35(5), pp. 652–667. [CrossRef] [PubMed]
Oshima, M. , Torii, R. , Tokuda, S. , Yamada, S. , and Koizumi, A. , 2012, “ Patient-Specific Modeling and Multi-Scale Blood Simulation for Computational Hemodynamic Study on the Human Cerebrovascular System,” Curr. Pharm. Biotechnol., 13(11), pp. 2153–2165. [CrossRef] [PubMed]
Alastruey, J. , Khir, A. W. , Matthys, K. S. , Segers, P. , Sherwin, S. J. , Verdonck, P. R. , Parker, K. P. , and Peiró, J. , 2011, “ Pulse Wave Propagation in a Model Human Arterial Network: Assessment of 1-D Visco-Elastic Simulations Against In Vitro Measurements,” J. Biomech., 44(12), pp. 2250–2258. [CrossRef] [PubMed]
Reymond, P. , Merenda, F. , Perren, F. , Rüfenacht, D. , and Stergiopulos, N. , 2009, “ Validation of a One-Dimensional Model of the Systemic Arterial Tree,” Am. J. Physiol.: Heart Circ. Physiol., 297(1), pp. H208–H222. [CrossRef] [PubMed]
Xiao, N. , Alastruey, J. , and Alberto Figueroa, C. , 2014, “ A Systematic Comparison Between 1-D and 3-D Hemodynamics in Compliant Arterial Models,” Int. J. Numer. Methods Biomed. Eng., 30(2), pp. 204–231. [CrossRef]
Liang, F. Y. , Takagi, S. , Himeno, R. , and Liu, H. , 2009, “ Multi-Scale Modeling of the Human Cardiovascular System With Applications to Aortic Valvular and Arterial Stenosis,” Med. Biol. Eng. Comput., 47(7), pp. 743–755. [CrossRef] [PubMed]
Müller, L. O. , and Toro, E. F. , 2014, “ A Global Multiscale Mathematical Model for the Human Circulation With Emphasis on the Venous System,” Int. J. Numer. Methods Biomed. Eng., 30(7), pp. 681–725. [CrossRef]
Mynard, J. P. , and Nithiarasu, P. , 2008, “ A 1D Arterial Blood Flow Model Incorporating Ventricular Pressure, Aortic Valve and Regional Coronary Flow Using the Locally Conservative Galerkin (LCG) Method,” Commun. Numer. Methods Eng., 24(5), pp. 367–417. [CrossRef]
Liang, F. Y. , Takagi, S. , Himeno, R. , and Liu, H. , 2009, “ Biomechanical Characterization of Ventricular–Arterial Coupling During Aging: A Multi-Scale Model Study,” J. Biomech., 42(6), pp. 692–704. [CrossRef] [PubMed]
Formaggia, L. , Lamponi, D. , Tuveri, M. , and Veneziani, A. , 2006, “ Numerical Modeling of 1D Arterial Networks Coupled With a Lumped Parameters Description of the Heart,” Comput. Methods Biomech. Biomed. Eng., 9(5), pp. 273–288. [CrossRef]
Tokuda, S. , 2007, “ Hemodynamic Simulation for Prediction of Initiation and Growth of Cardiovascular Diseases,” Ph.D. thesis, The University of Tokyo, Bunkyo, Tokyo.
Cousins, W. , and Gremaud, P. A. , 2012, “ Boundary Conditions for Hemodynamics: The Structured Tree Revisited,” J. Comput. Phys., 231(18), pp. 6086–6096. [CrossRef]
Reymond, P. , Bohraus, Y. , Perren, F. , Lazeyras, F. , and Stergiopulos, N. , 2011, “ Validation of a Patient-Specific One-Dimensional Model of the Systemic Arterial Tree,” Am. J. Physiol.: Heart Circ. Physiol., 301(3), pp. H1173–H1182. [CrossRef] [PubMed]
Cebral, J. R. , Castro, M. A. , Burgess, J. E. , Pergolizzi, R. S. , Sheridan, M. J. , and Putman, C. M. , 2005, “ Characterization of Cerebral Aneurysms for Assessing Risk of Rupture by Using Patient-Specific Computational Hemodynamics Models,” Am. J. Neuroradiology, 26(10), pp. 2550–2559.
Cebral, J. R. , Mut, F. , Weir, J. , and Putman, C. M. , 2011, “ Association of Hemodynamic Characteristics and Cerebral Aneurysm Rupture,” Am. J. Neuroradiology, 32(2), pp. 264–270. [CrossRef]
Chung, B. , and Cebral, J. R. , 2015, “ CFD for Evaluation and Treatment Planning of Aneurysms: Review of Proposed Clinical Uses and Their Challenges,” Ann. Biomed. Eng., 43(1), pp. 122–138. [CrossRef] [PubMed]
Mut, F. , Ruijters, D. , Babic, D. , Bleise, C. , Lylyk, P. , and Cebral, J. R. , 2014, “ Effects of Changing Physiologic Conditions on the in vivo Quantification of Hemodynamic Variables in Cerebral Aneurysms Treated With Flow Diverting Devices,” Int. J. Numer. Methods Biomed. Eng., 30(1), pp. 135–142. [CrossRef]
Pereira, V. M. , Brina, O. , Marcos Gonzales, A. , Narata, A. P. , Bijlenga, P. , Schaller, K. , and Ouared, R. , 2013, “ Evaluation of the Influence of Inlet Boundary Conditions on Computational Fluid Dynamics for Intracranial Aneurysms: A Virtual Experiment,” J. Biomech., 46(9), pp. 1531–1539. [CrossRef] [PubMed]
Xiang, J. , Siddiqui, A. H. , and Meng, H. , 2014, “ The Effect of Inlet Waveforms on Computational Hemodynamics of Patient-Specific Intracranial Aneurysms,” J. Biomech., 47, pp. 3882–3890. [CrossRef] [PubMed]
Evju, Ø. , Valen-Sendstad, K. , and Mardal, K. A. , 2013, “ A Study of Wall Shear Stress in 12 Aneurysms With Respect to Different Viscosity Models and Flow Conditions,” J. Biomech., 46(16), pp. 2802–2808. [CrossRef] [PubMed]
Westerhof, N. , Sipkema, P. , Van Den Bos, G. C. , and Elzinga, G. , 1972, “ Forward and Backward Waves in the Arterial System,” Cardiovasc. Res., 6(6), pp. 648–656. [CrossRef] [PubMed]
Valencia, A. , Munoz, F. , Araya, S. , Rivera, R. , and Bravo, E. , 2009, “ Comparison Between Computational Fluid Dynamics, Fluid–Structure Interaction and Computational Structural Dynamics Predictions of Flow-Induced Wall Mechanics in an Anatomically Realistic Cerebral Aneurysm Model,” Int. J. Comput. Fluid Dyn., 23(9), pp. 649–666. [CrossRef]
Tricerri, P. , Dedè, L. , Deparis, S. , Quarteroni, A. , Robertson, A. M. , and Sequeira, A. , 2015, “ Fluid–Structure Interaction Simulations of Cerebral Arteries Modeled by Isotropic and Anisotropic Constitutive Laws,” Comput. Mech., 55, pp. 479–498. [CrossRef]
Torii, R. , Oshima, M. , Kobayashi, T. , Takagi, K. , and Tezduyar, T. E. , 2011, “ Influencing Factors in Image-Based Fluid–Structure Interaction Computation of Cerebral Aneurysms,” Int. J. Numer. Methods Fluids, 65(1–3), pp. 324–340. [CrossRef]
Torii, R. , Oshima, M. , Kobayashi, T. , Takagi, K. , and Tezduyar, T. E. , 2010, “ Influence of Wall Thickness on Fluid–Structure Interaction Computations of Cerebral Aneurysms,” Int. J. Numer. Methods Biomed. Eng., 26(3–4), pp. 336–347. [CrossRef]
David, T. , Brown, M. , and Ferrandez, A. , 2003, “ Auto-Regulation and Blood Flow in the Cerebral Circulation,” Int. J. Numer. Methods Fluids, 43(6–7), pp. 701–713. [CrossRef]
Kim, H. J. , Vignon-Clementel, I. E. , Coogan, J. S. , Figueroa, C. A. , Jansen, K. E. , and Taylor, C. A. , 2010, “ Patient-Specific Modeling of Blood Flow and Pressure in Human Coronary Arteries,” Ann. Biomed. Eng., 38(10), pp. 3195–3209. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 2

Illustration of the coupling between the 1D model and the 3D model through a 0D interface model

Grahic Jump Location
Fig. 1

Schematic description of the multiscale modeling system, where a 3D model of the MCA bifurcation is embedded in a 0-1-D multiscale model of the entire cardiovascular system

Grahic Jump Location
Fig. 3

Flowchart of coupling computation (see Appendix A for a detailed description of the exchanges of hemodynamic quantities among models)

Grahic Jump Location
Fig. 4

Results computed by a multiscale model of a 3D MCA bifurcation coupled with the cardiovascular system and comparisons with the results obtained by the original 0-1-D model before the 3D model is embedded: (a) flow velocity contour in the center plane of the MCA bifurcation, (b) history of convergence error of coupling computation, and (c/d) comparisons of computed flow/pressure waveforms at the boundaries of the MCA bifurcation between the multiscale model and the 0-1-D model

Grahic Jump Location
Fig. 7

Comparisons of computed flow waveforms at the ACA and MCA outlets between the multiscale model and the stand-alone 3D model. The results of multiscale computation and stand-alone 3D computation are represented by solid lines and dashed dotted lines, respectively. The preoperative and postoperative results are shown on the upper and lower panels, respectively. Note that the same inflow conditions (flow waveform imposed at the ICA inlet) have been prescribed for the multiscale model and the stand-alone 3D model.

Grahic Jump Location
Fig. 8

Velocity vectors in a plane cutting through the MCA bifurcation visualized based on the multiscale computation (upper panel) and stand-alone 3D computation (lower panel). The preoperative and postoperative results are shown on the left and right sides, respectively. The arrow lines indicate the directions of blood flows in the near bifurcation region.

Grahic Jump Location
Fig. 5

Flow (upper panel, left) and pressure (upper panel, right) waveforms at the boundaries of the MCA bifurcation computed by the multiscale model (the thick solid lines represent the preoperative results, whereas the thin dashed lines denote the postoperative results) and the DSA images taken before (lower panel, left) and after (lower panel, right) the operation. The DSA images show that the direction of flow in the left ACA changed after the operation.

Grahic Jump Location
Fig. 6

Spatial distributions of TAWSS and OSI on the walls of the MCA bifurcation obtained from multiscale computations (upper panel) and stand-alone 3D computations (lower panel). The preoperative and postoperative results are shown on the left and right sides, respectively.



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