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Journal articles on the topic 'Blood flow - Computer simulation'

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Published: 4 June 2021

Last updated: 7 February 2022

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1

Tsubota, Ken-ichi, Shigeo Wada, and Takami Yamaguchi. "A Particle Method Computer Simulation of the Blood Flow(Micro- and Nano-biomechanics)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2004.1 (2004): 241–42. http://dx.doi.org/10.1299/jsmeapbio.2004.1.241.

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2

Goldfarb-Rumyantzev, Alexander, Chaim Charytan, and Bruce Spinovitz. "Computer simulation of blood flow through a dialyzer/hemofilter." American Journal of Kidney Diseases 27, no. 4 (April 1996): A7. http://dx.doi.org/10.1016/s0272-6386(96)90202-4.

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3

Balar, Salil D., T. R. Rogge, and D. F. Young. "Computer simulation of blood flow in the human arm." Journal of Biomechanics 22, no. 6-7 (January 1989): 691–97. http://dx.doi.org/10.1016/0021-9290(89)90019-5.

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4

Burnette, Ronald R. "Computer simulation of human blood flow and vascular resistance." Computers in Biology and Medicine 26, no. 5 (September 1996): 363–69. http://dx.doi.org/10.1016/0010-4825(96)00017-0.

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5

Bartesaghi, Simone, and Giorgio Colombo. "Embedded CFD Simulation for Blood Flow." Computer-Aided Design and Applications 10, no. 4 (January 2013): 685–99. http://dx.doi.org/10.3722/cadaps.2013.685-699.

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6

TSUBOTA, Ken-ichi, Shigeo WADA, and Takami YAMAGUCHI. "A Direct Computer Simulation of Blood Flow using Particle Method." Journal of the Visualization Society of Japan 25, Supplement1 (2005): 111–12. http://dx.doi.org/10.3154/jvs.25.supplement1_111.

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7

Wada, S., Y. Kitagawa, K. i. Tsubota, and T. Yamaguchi. "Modeling and computer simulation of elastic red blood cell flow." Journal of Biomechanics 39 (January 2006): S440. http://dx.doi.org/10.1016/s0021-9290(06)84795-0.

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8

Zonnebeld, Niek, Wouter Huberts, Magda M. van Loon, Tammo Delhaas, and Jan H. M. Tordoir. "Preoperative computer simulation for planning of vascular access surgery in hemodialysis patients." Journal of Vascular Access 18, no. 1_suppl (March 2017): S118—S124. http://dx.doi.org/10.5301/jva.5000661.

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Introduction The arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis patients. Unfortunately, 20-40% of all constructed AVFs fail to mature (FTM), and are therefore not usable for hemodialysis. AVF maturation importantly depends on postoperative blood volume flow. Predicting patient-specific immediate postoperative flow could therefore support surgical planning. A computational model predicting blood volume flow is available, but the effect of blood flow predictions on the clinical endpoint of maturation (at least 500 mL/min blood volume flow, diameter of the venous cannulation segment ≥4 mm) remains undetermined. Methods A multicenter randomized clinical trial will be conducted in which 372 patients will be randomized (1:1 allocation ratio) between conventional healthcare and computational model-aided decision making. All patients are extensively examined using duplex ultrasonography (DUS) during preoperative assessment (12 venous and 11 arterial diameter measurements; 3 arterial volume flow measurements). The computational model will predict patient-specific immediate postoperative blood volume flows based on this DUS examination. Using these predictions, the preferred AVF configuration is recommended for the individual patient (radiocephalic, brachiocephalic, or brachiobasilic). The primary endpoint is FTM rate at six weeks in both groups, secondary endpoints include AVF functionality and patency rates at 6 and 12 months postoperatively. Trial registration ClinicalTrials.gov (NCT02453412), and ToetsingOnline.nl (NL51610.068.14).

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9

Tsubota, Ken-ichi, Shigeo Wada, and Takami Yamaguchi. "Particle method for computer simulation of red blood cell motion in blood flow." Computer Methods and Programs in Biomedicine 83, no. 2 (August 2006): 139–46. http://dx.doi.org/10.1016/j.cmpb.2006.06.005.

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10

Lou, Zheng, and Wen-Jei Yang. "A Computer Simulation of the Blood Flow at the Aortic Bifurcation." Bio-Medical Materials and Engineering 1, no. 3 (1991): 173–93. http://dx.doi.org/10.3233/bme-1991-1306.

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11

Sampson, Michael G., Paul K. C. Wong, K. Wayne Johnston, and C. Ross Ethier. "Computer simulation of blood flow patterns in arteries of various geometries." Journal of Vascular Surgery 14, no. 5 (November 1991): 658–67. http://dx.doi.org/10.1067/mva.1991.30221.

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12

Wong, Paul K. C., K. Wayne Johnston, C. Ross Ethier, and Richard S. C. Cobbold. "Computer simulation of blood flow patterns in arteries of various geometries." Journal of Vascular Surgery 14, no. 5 (November 1991): 658–67. http://dx.doi.org/10.1016/0741-5214(91)90190-6.

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13

Glenny, R. W., and H. T. Robertson. "A computer simulation of pulmonary perfusion in three dimensions." Journal of Applied Physiology 79, no. 1 (July 1, 1995): 357–69. http://dx.doi.org/10.1152/jappl.1995.79.1.357.

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Pulmonary perfusion is spatially correlated with neighboring regions of lung having similar magnitudes of flow and distant pieces exhibiting negative correlation. Although local correlation has been noted in a wide variety of natural processes, negative correlation has not and it may be unique to organ blood flow. We investigate the regional perfusion predicted by a three-dimensional branching vascular model to determine whether such a model can create negative correlation of perfusion. The distribution of flows is modeled by a dichotomously branching tree in which the fraction of flow from parent to daughter branches is gamma and 1-gamma at each bifurcation. The flow asymmetry parameter (gamma) is randomly chosen for each bifurcation from a normal distribution with a mean of 0.5 with an SD of sigma. The branches branch along one of three orthogonal directions to assure a space-filling structure. This model produces flow distributions similar to those observed in experimental animals, with perfusion being positively correlated locally and negatively correlated at distance. The model is refined by incorporating an effect of gravity, which redirects a fraction (delta), of the flow against gravity to the companion daughter branch in the gravitational direction. A flow bias in the “dorsal” direction is also introduced to account for differences in supine-prone perfusion gradients. In its final form, this three-dimensional branching model accounts for previously observed 1) spatial correlation of regional perfusion with negative correlation over distance, 2) isogravitational perfusion heterogeneity, 3) differences in supine and prone perfusion gradients, 4) positive correlation of flows between supine and prone postures, 5) relatively small contributions of gravity to perfusion heterogeneity, and 6) fractal distributions of flow. This three-dimensional branching vascular model relates the function and structure of the pulmonary vascular tree, offering an explanation for both heterogeneous and spatially correlated regional flows.

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14

Neglia, D., G. Ferrari, F. Bernini, M. Micalizzi, A. L’Abbate, M. G. Trivella, and C. De Lazzari. "Computer Simulation of Coronary Flow Waveforms during Caval Occlusion." Methods of Information in Medicine 48, no. 02 (2009): 113–22. http://dx.doi.org/10.3414/me0539.

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Summary Objectives: Mathematical modeling of the cardiovascular system is a powerful tool to extract physiologically relevant information from multi-parametric experiments. The purpose of the present work was to reproduce by means of a computer simulator, systemic and coronary measurements obtained by in vivo experiments in the pig. Methods: We monitored in anesthetized open-chest pig the phasic blood flow of the left descending coronary artery, aortic pressure, left ventricular pressure and volume. Data were acquired before, during, and after caval occlusion.Inside the software simulator (CARDIOSIM©) of the cardiovascular system, coronary circulation was modeled in three parallel branching sections. Both systemic and pulmonary circulations were simulated using a lumped parameter mathematical model. Variable elastance model reproduced Starling’s law of the heart. Results: Different left ventricular pressure-volume loops during experimental caval occlusion and simulated cardiac loops are presented. The sequence of coronary flow-aortic pressure loops obtained in vivo during caval occlusion together with the simulated loops reproduced by the software simulator are reported. Finally experimental and simulated instantaneous coronary blood flow waveforms are shown. Conclusions: The lumped parameter model of the coronary circulation, together with the cardiovascular system model, is capable of reproducing the changes during caval occlusion, with the profound shape deformation of the flow signal observed during the in vivo experiment. In perspectives, the results of the present model could offer new tool for studying the role of the different determinants of myocardial perfusion, by using the coronary loop shape as a “sensor” of ventricular mechanics in various physiological and pathophysiological conditions.

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15

Vierendeels, J. A., K. Riemslagh, E. Dick, and P. R. Verdonck. "Computer Simulation of Intraventricular Flow and Pressure Gradients During Diastole." Journal of Biomechanical Engineering 122, no. 6 (July 9, 2000): 667–74. http://dx.doi.org/10.1115/1.1318941.

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A two-dimensional axisymmetric computer model is developed for the simulation of the filling flow in the left ventricle (LV). The computed results show that vortices are formed during the acceleration phases of the filling waves. During the deceleration phases these are amplified and convected into the ventricle. The ratio of the maximal blood velocity at the mitral valve (peak E velocity) to the flow wave propagation velocity (WPV) of the filling wave is larger than 1. This hemodynamic behavior is also observed in experiments in vitro (Steen and Steen, 1994, Cardiovasc. Res., 28, pp. 1821–1827) and in measurements in vivo with color M-mode Doppler echocardiography (Stugaard et al., 1994, J. Am. Coll. Cardiol., 24, 663–670). Computed intraventricular pressure profiles are similar to observed profiles in a dog heart (Courtois et al., 1988, Circulation, 78, pp. 661–671). The long-term goal of the computer model is to study the predictive value of noninvasive parameters (e.g., velocities measured with Doppler echocardiography) on invasive parameters (e.g., pressures, stiffness of cardiac wall, time constant of relaxation). Here, we show that higher LV stiffness results in a smaller WPV for a given peak E velocity. This result may indicate an inverse relationship between WPV and LV stiffness, suggesting that WPV may be an important noninvasive index to assess LV diastolic stiffness, LV diastolic pressure and thus atrial pressure (preload). [S0148-0731(00)01606-X]

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16

TSUBOTA, Ken-ichi, Hiroki KAMADA, Shigeo WADA, and Takami YAMAGUCHI. "2105 A Particle Method Computer Simulation of Blood Cells Motion Considering Nonuniformity of Blood Flow." Proceedings of The Computational Mechanics Conference 2005.18 (2005): 57–58. http://dx.doi.org/10.1299/jsmecmd.2005.18.57.

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17

HIAI, YASUHIRO, TAKAO TAKADA, YOSHIO SONODA, NOBUYUKI MORIBE, NOBORU KATSUDA, MASAHIRO HATEMURA, and MUTSUMASA TAKAHASHI. "496. MR angiography : An attempt of blood flow density with computer simulation." Japanese Journal of Radiological Technology 47, no. 8 (1991): 1526. http://dx.doi.org/10.6009/jjrt.kj00003324235.

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18

LEUPRECHT, ARMIN, and KARL PERKTOLD. "Computer Simulation of Non-Newtonian Effects on Blood Flow in Large Arteries." Computer Methods in Biomechanics and Biomedical Engineering 4, no. 2 (January 2001): 149–63. http://dx.doi.org/10.1080/10255840008908002.

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19

Bourantas, G. C., D. S. Lampropoulos, B. F. Zwick, V. C. Loukopoulos, A. Wittek, and K. Miller. "Immersed boundary finite element method for blood flow simulation." Computers & Fluids 230 (November 2021): 105162. http://dx.doi.org/10.1016/j.compfluid.2021.105162.

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20

de Hoon, N., R. van Pelt, A. Jalba, and A. Vilanova. "4D MRI Flow Coupled to Physics-Based Fluid Simulation for Blood-Flow Visualization." Computer Graphics Forum 33, no. 3 (June 2014): 121–30. http://dx.doi.org/10.1111/cgf.12368.

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21

KANAI, Ryoma, and Ken-ichi TSUBOTA. "Computer simulation of blood flow in micro channel network according to viscoelasticity of red blood cells." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2018.30 (2018): 1F03. http://dx.doi.org/10.1299/jsmebio.2018.30.1f03.

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22

TSUBOTA, Ken-ichi, Hiroki KAMADA, Shigeo WADA, and Takami YAMAGUCHI. "Mechanical interaction among blood cells in blood flow predicted by computer simulation using a particle method." Proceedings of The Computational Mechanics Conference 2004.17 (2004): 69–70. http://dx.doi.org/10.1299/jsmecmd.2004.17.69.

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23

Bodys, Jakub, Jakub Poraj, and Maciej Kryś. "Blood flow in cerebral arteries – automated way from Computed Tomography to ANSYS Fluent." Advanced Technologies in Mechanics 2, no. 1(2) (July 7, 2015): 9. http://dx.doi.org/10.17814/atim.2015.1(2).13.

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<p style="text-align: justify;">With the constant growth of computer simulation significance in science and engi-neering, many new fields are gaining access to these powerful tools. One of these new disciplines is medicine. Human body provides many fascinating areas that could be researched from completely different angle and could gain all the benefits that computer simulation offers. For example blood flow in human arteries can be stud-ied using Computational Fluid Dynamics. Researchers of cerebrovascular disorders can get an insight view on physical phenomena of blood flow and study risk factors of embolism or cerebral aneurysm.</p><p style="text-align: justify;">Main issue in using computer simulation in medical research is the complexity and uniqueness of geometry that needs to be handled. After all, human body is one of the most sophisticated engineering systems created by nature. In this paper, a workflow for creating a numerical mesh for CFD simulation purposes is shown. Application shown in the example focus on cerebral arteries blood flow simulation. Numerical mesh is generated based on CT scan of patient’s head, using freeware tools Slicer3D and AutoIt3 as well as commercial software ANSYS Fluent Meshing 15.0.</p>

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24

Lou, Zheng, and Wen-Jei Yang. "A computer simulation of the non-Newtonian blood flow at the aortic bifurcation." Journal of Biomechanics 26, no. 1 (January 1993): 37–49. http://dx.doi.org/10.1016/0021-9290(93)90611-h.

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25

Miraucourt, Olivia, Stéphanie Salmon, Marcela Szopos, and Marc Thiriet. "Blood flow in the cerebral venous system: modeling and simulation." Computer Methods in Biomechanics and Biomedical Engineering 20, no. 5 (November 1, 2016): 471–82. http://dx.doi.org/10.1080/10255842.2016.1247833.

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26

Schenkel, A., M. O. Deville, M. L. Sawley, P. Hagmann, and J. D. Rochat. "Flow simulation and hemolysis modeling for a blood centrifuge device." Computers & Fluids 86 (November 2013): 185–98. http://dx.doi.org/10.1016/j.compfluid.2013.06.019.

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27

Filipovic, Nenad, and Milos Kojic. "Computer simulations of blood flow with mass transport through the carotid artery bifurcation." Theoretical and Applied Mechanics 31, no. 1 (2004): 1–33. http://dx.doi.org/10.2298/tam0401001f.

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The current paradigm for clinical diagnostic for the treatment of vascular disease relies exclusively on diagnostic imaging data to define the present state of the patient, empirical data to evaluate the efficacy of prior treatments for similar patients. These techniques are insufficient to predict the outcome of a given treatment for an individual patient. We here propose a new paradigm of predictive medicine where physician could use computational simulation to construct and evaluate a specific geometrical/anatomical model to predict the outcome for an individual patient. For this purpose it is necessary to develop a complex software system which combines user friendly interface, automatic solid modeling, automatic finite mesh generation, computational fluid dynamics and post-processing visualization. The flow dynamics is defined according to the incompressible Navier-Stokes equations for Newtonian and non-Newtonian fluids. Mass transport of oxygen and macromolecules is modeled by the convection diffusion equation and coupled with flow dynamics. The computer simulations are based upon finite element analysis where the new computer methods for coupling oxygen transport and fluid flow are described. The comparison results shows a good agreement between clinical observation for critical zones of flow separation, flow recirculation, low wall shear stresses which may contribute to the development of atherosclerotic diseases.

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28

Steinman, Dolores A. Hangan, and David A. Steinman. "The Art and Science of Visualizing Simulated Blood-Flow Dynamics." Leonardo 40, no. 1 (February 2007): 71–76. http://dx.doi.org/10.1162/leon.2007.40.1.71.

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The increasing use of computer enhancement and simulation to reveal the unseen human body brings with it challenges, opportunities and responsibilities at the interface of art and science. Here they are presented and discussed in the context of efforts to understand the role of blood-flow dynamics in vascular disease.

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29

NIU, YANG-YAO, and SHOU-CHENG TCHENG. "COMPUTATIONS OF PULSATILE AORTIC BLOOD FLOW PROBLEMS ON PARALLEL COMPUTERS." Biomedical Engineering: Applications, Basis and Communications 15, no. 03 (June 25, 2003): 109–14. http://dx.doi.org/10.4015/s1016237203000171.

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In this study, a parallel computing technology is applied on the simulation of aortic blood flow problems. A third-order upwind flux extrapolation with a dual-time integration method based on artificial compressibility solver is used to solve the Navier-Stokes equations. The original FORTRAN code is converted to the MPI code and tested on a 64-CPU IBM SP2 parallel computer and a 32-node PC Cluster. The test results show that a significant reduction of computing time in running the model and a super-linear speed up rate is achieved up to 32 CPUs at PC cluster. The speed up rate is as high as 49 for using IBM SP2 64 processors. The test shows very promising potential of parallel processing to provide prompt simulation of the current aortic flow problems.

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30

TSUBOTA, Kenichi, Shigeo WADA, and Takami YAMAGUCHI. "Computer simulation using particle method for coupled problem of blood flow and deformation of red blood cell." Proceedings of The Computational Mechanics Conference 2003.16 (2003): 297–98. http://dx.doi.org/10.1299/jsmecmd.2003.16.297.

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31

Lou, Zheng, and Wen-Jei Yang. "A Computer Simulation of the Blood Flow at the Aortic Bifurcation With Flexible Walls." Journal of Biomechanical Engineering 115, no. 3 (August 1, 1993): 306–15. http://dx.doi.org/10.1115/1.2895491.

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To understand the role of fluid dynamics in atherogenesis, especially the effect of the flexibility of arteries, a two-dimensional numerical model for blood flow at the aortic bifurcation with linear viscoelastic walls is developed. The arbitrary Lagrangian-Eulerian method is adopted to deal with the moving boundary problem. The wall expansion induces flow reversals or eddies during the decelerating systole while the wall contraction restricts them during the diastole. A flexible bifurcation experiences the shear stresses about 10 percent lower than those of a rigid one.

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32

Zhao, Chunzhang. "NUMERICAL SIMULATION OF FLOW FIELD IN A MICROAXIAL BLOOD PUMP." Chinese Journal of Mechanical Engineering 41, no. 07 (2005): 19. http://dx.doi.org/10.3901/jme.2005.07.019.

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33

TAKAHASHI, Wataru, Ken-ichi TUBOTA, and Hirosi LIU. "9E-18 2D computer simulation of blood flow in microvessel network using particle method." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2010.23 (2011): 535–36. http://dx.doi.org/10.1299/jsmebio.2010.23.535.

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34

Charbel, F. T., M. Misra, M. E. Clarke, and J. I. Ausman. "Computer simulation of cerebral blood flow in Moyamoya and the results of surgical therapies." Clinical Neurology and Neurosurgery 99 (October 1997): S68—S73. http://dx.doi.org/10.1016/s0303-8467(97)00073-5.

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35

Nakamura, Masanori, Daisuke Mori, Shigeo Wada, Kenichi Tsubota, and Takami Yamaguchi. "Computer simulation of a blood flow in a left ventricle-aortic arch integrated model." Proceedings of The Computational Mechanics Conference 2003.16 (2003): 289–90. http://dx.doi.org/10.1299/jsmecmd.2003.16.289.

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36

Ju, Meongkeun, Swe Soe Ye, Bumseok Namgung, Seungkwan Cho, Hong Tong Low, Hwa Liang Leo, and Sangho Kim. "A review of numerical methods for red blood cell flow simulation." Computer Methods in Biomechanics and Biomedical Engineering 18, no. 2 (April 14, 2013): 130–40. http://dx.doi.org/10.1080/10255842.2013.783574.

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37

Papamanolis, L., H. J. Kim, C. Jaquet, M. Sinclair, M. Schaap, I. Danad, P. van Diemen, et al. "Patient-specific, multiscale, myocardial blood flow simulation for coronary artery disease." Computer Methods in Biomechanics and Biomedical Engineering 23, sup1 (October 19, 2020): S218—S220. http://dx.doi.org/10.1080/10255842.2020.1813433.

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38

Obrist, Walter D., Zihong Zhang, and Howard Yonas. "Effect of Xenon-Induced Flow Activation on Xenon-Enhanced Computed Tomography Cerebral Blood Flow Calculations." Journal of Cerebral Blood Flow & Metabolism 18, no. 11 (November 1998): 1192–95. http://dx.doi.org/10.1097/00004647-199811000-00005.

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Computer simulations of stable xenon (sXe) uptake curves were used to evaluate the effect of xenon-induced flow activation on CBF calculations by xenon-enhanced computed tomography, Estimates of flow activation were based on repeated transcranial Doppler measurements of blood velocity during 4,5 minutes of sXe inhalation, The synthetic curves were generated from a generalized Kety equation that included time-varying blood flow activation, In contrast to the peak 35% increase in blood flow velocity during sXe inhalation, a standard analysis of the flow-varying synthetic curves revealed only minor 3% to 5% increases in calculated CBF. It is concluded that brief xenon inhalations can provide blood flow estimates that contain minimal bias from activation.

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39

Bora, Şebnem, Vedat Evren, Sevcan Emek, and Ibrahim Çakırlar. "Agent-based modeling and simulation of blood vessels in the cardiovascular system." SIMULATION 95, no. 4 (June 9, 2017): 297–312. http://dx.doi.org/10.1177/0037549717712602.

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The purpose of this study is to develop a model to simulate the behavior of the human cardiovascular system for use in medical education. The proposed model ensures that the output of the system is accurately represented in both equilibrium conditions and imbalance conditions including in the presence of adaptive agents. In this study, field experts develop an agent-based blood vessel model, i.e., a submodel for the stated purpose. In the proposed blood vessel model, vessels are represented by agents whereas blood flow is represented by the interaction between agents. Adaptive behavior shown by vessels in terms of resistance to the blood flow is defined by the agents’ properties, which are used as the basis for calculating and graphically representing the physical parameters of blood flow, specifically blood pressure, blood flow velocity, and the resistance of the vessel. The adaptation of the vessel agents is supported by a case study, which demonstrates the adaptive behavior of the blood vessel agents through a negative feedback control mechanism. The blood vessel model proposed is flexible in nature such that it can be adapted to account for the behavior of the vessel sections in any vascular structure.

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40

Alowayyed, S., G. Závodszky, V. Azizi, and A. G. Hoekstra. "Load balancing of parallel cell-based blood flow simulations." Journal of Computational Science 24 (January 2018): 1–7. http://dx.doi.org/10.1016/j.jocs.2017.11.008.

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41

Wei, Fei, John Westerdale, Eileen M. McMahon, Marek Belohlavek, and Jeffrey J. Heys. "Weighted Least-Squares Finite Element Method for Cardiac Blood Flow Simulation with Echocardiographic Data." Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/371315.

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As both fluid flow measurement techniques and computer simulation methods continue to improve, there is a growing need for numerical simulation approaches that can assimilate experimental data into the simulation in a flexible and mathematically consistent manner. The problem of interest here is the simulation of blood flow in the left ventricle with the assimilation of experimental data provided by ultrasound imaging of microbubbles in the blood. The weighted least-squares finite element method is used because it allows data to be assimilated in a very flexible manner so that accurate measurements are more closely matched with the numerical solution than less accurate data. This approach is applied to two different test problems: a flexible flap that is displaced by a jet of fluid and blood flow in the porcine left ventricle. By adjusting how closely the simulation matches the experimental data, one can observe potential inaccuracies in the model because the simulation without experimental data differs significantly from the simulation with the data. Additionally, the assimilation of experimental data can help the simulation capture certain small effects that are present in the experiment, but not modeled directly in the simulation.

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42

Chen, Tong, Xudong Liu, Biao Si, Yong Feng, Huifeng Zhang, Bing Jia, and Shengzhang Wang. "Comparison between Single-Phase Flow Simulation and Multiphase Flow Simulation of Patient-Specific Total Cavopulmonary Connection Structures Assisted by a Rotationally Symmetric Blood Pump." Symmetry 13, no. 5 (May 20, 2021): 912. http://dx.doi.org/10.3390/sym13050912.

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To accurately assess the hemolysis risk of the ventricular assist device, this paper proposed a cell destruction model and the corresponding evaluation parameters based on multiphase flow. The single-phase flow and multiphase flow in two patient-specific total cavopulmonary connection structures assisted by a rotationally symmetric blood pump (pump-TCPC) were simulated. Then, single-phase and multiphase cell destruction models were used to evaluate the hemolysis risk. The results of both cell destruction models indicated that the hemolysis risk in the straight pump-TCPC model was lower than that in the curved pump-TCPC model. However, the average and maximum values of the multiphase flow blood damage index (mBDI) were smaller than those of the single-phase flow blood damage index (BDI), but the average and maximum values of the multiphase flow particle residence time (mPRT) were larger than those of the single-phase flow particle residence time (PRT). This study proved that the multiphase flow method can be used to simulate the mechanical behavior of red blood cells (RBCs) and white blood cells (WBCs) in a complex flow field and the multiphase flow cell destruction model had smaller estimates of the impact shear stress.

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43

Gao, Lian, Yufeng Zhang, Kexin Zhang, Guanghui Cai, Junhua Zhang, and Xinling Shi. "A computer simulation model for Doppler ultrasound signals from pulsatile blood flow in stenosed vessels." Computers in Biology and Medicine 42, no. 9 (September 2012): 906–14. http://dx.doi.org/10.1016/j.compbiomed.2012.07.002.

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44

Caballero, A. D., and S. Laín. "Numerical simulation of non-Newtonian blood flow dynamics in human thoracic aorta." Computer Methods in Biomechanics and Biomedical Engineering 18, no. 11 (February 24, 2014): 1200–1216. http://dx.doi.org/10.1080/10255842.2014.887698.

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45

Oyler, George A., Robert B. Duckrow, and Richard A. Hawkins. "Computer simulation of the blood-brain barrier: a model including two membranes, blood flow, facilitated and non-facilitated diffusion." Journal of Neuroscience Methods 44, no. 2-3 (September 1992): 179–96. http://dx.doi.org/10.1016/0165-0270(92)90010-b.

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Oshima, Marie, Ryo Torii, Toshio Kobayashi, Nobuyuki Taniguchi, and Kiyoshi Takagi. "Finite element simulation of blood flow in the cerebral artery." Computer Methods in Applied Mechanics and Engineering 191, no. 6-7 (December 2001): 661–71. http://dx.doi.org/10.1016/s0045-7825(01)00307-3.

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Eulzer, P., M. Meuschke, C. M. Klingner, and K. Lawonn. "Visualizing Carotid Blood Flow Simulations for Stroke Prevention." Computer Graphics Forum 40, no. 3 (June 2021): 435–46. http://dx.doi.org/10.1111/cgf.14319.

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Kazantsev, A. N., R. A. Vinogradov, Yu N. Zakharov, V. G. Borisov, M. A. Chernyavsky, V. N. Kravchuk, D. V. Shmatov, et al. "Prediction of Resthenosis After Carotid Endarterectomy by the Method of Computer Simulation." Russian Sklifosovsky Journal "Emergency Medical Care" 10, no. 2 (August 24, 2021): 401–7. http://dx.doi.org/10.23934/2223-9022-2021-10-2-401-407.

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The article describes a computer modeling technique that allows predicting the development of restenosis of the internal carotid artery after carotid endarterectomy (CEE). A clinical case has been demonstrated that proves the effectiveness of the developed method. It is indicated that for the correct formation of the geometric model, data from multispiral computed tomography with angiography of the patient after CEE with a layer thickness of 0.6 mm and a current of 355 mA are required. To build a flow model, data of color duplex scanning in three sections are required: 1. In the proximal section of the common carotid artery (3 cm proximal to the bifurcation); 2. In the section of the external carotid artery, 2 cm distal to the carotid sinus; 3. In the section of the internal carotid artery, 2 cm distal to the carotid sinus. The result of computer calculations using specialized software (Sim Vascular, Python, Open Foam) is a mathematical model of blood flow in a vessel. It is an array of calculated data describing the speed and other characteristics of the flow at each point of the artery. Based on the analysis of RRT and TAWSS indicators, a computer model of bifurcation is formed, which makes it possible to predict areas of increased risk of restenosis development. Thus, the developed technique is able to identify a cohort of patients after CEE, subjected to a high probability of loss of the vessel lumen. Such an opportunity will provide a more precise supervision of these patients in the postoperative period with the aim of early diagnosis of restenosis and timely prevention of the development of adverse cardiovascular events.

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Tonar, Zbyněk, Petra Kochová, Robert Cimrman, Kirsti Witter, Jiří Janáček, and Vladimír Rohan. "Microstructure Oriented Modelling of Hierarchically Perfused Porous Media for Cerebral Blood Flow Evaluation." Key Engineering Materials 465 (January 2011): 286–89. http://dx.doi.org/10.4028/www.scientific.net/kem.465.286.

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We used immunochemistry, light microscopy and stereological methods for quantitative description of the microvascular network in 13 tissue samples of the human brain. While the tortuosity of microvessels was comparable in all brain parts under study, the length density of microvessels was higher in subcortical grey matter (652.5±162.0 mm-2) and in the cortex (570.9±71.8 mm-2) than in the white matter (152.7±42.0 mm-2). The numerical density of microvessels was higher in subcortical grey matter (3782.0±1602.0 mm-3) and cerebral cortex (3160.0±638.4 mm-3) than in white matter (627.7±318.5 mm-3). We developed simulation software gensei which generates series of images representing three-dimensional models of microvessels with known length density, volume fraction, and surface density. The simulations are statistically similar to real microvessel networks and can be used for computer modelling of brain perfusion.

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Afrouzi, Hamid Hassanzadeh, Majid Ahmadian, Mirollah Hosseini, Hossein Arasteh, Davood Toghraie, and Sara Rostami. "Simulation of blood flow in arteries with aneurysm: Lattice Boltzmann Approach (LBM)." Computer Methods and Programs in Biomedicine 187 (April 2020): 105312. http://dx.doi.org/10.1016/j.cmpb.2019.105312.

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