Cardiovascular Engineering and Technology

, Volume 7, Issue 2, pp 148–158 | Cite as

A High Performance Pulsatile Pump for Aortic Flow Experiments in 3-Dimensional Models

  • Rafeed A. Chaudhury
  • Victor Atlasman
  • Girish Pathangey
  • Nicholas Pracht
  • Ronald J. Adrian
  • David H. Frakes
Article

Abstract

Aortic pathologies such as coarctation, dissection, and aneurysm represent a particularly emergent class of cardiovascular diseases. Computational simulations of aortic flows are growing increasingly important as tools for gaining understanding of these pathologies, as well as for planning their surgical repair. In vitro experiments are required to validate the simulations against real world data, and the experiments require a pulsatile flow pump system that can provide physiologic flow conditions characteristic of the aorta. We designed a newly capable piston-based pulsatile flow pump system that can generate high volume flow rates (850 mL/s), replicate physiologic waveforms, and pump high viscosity fluids against large impedances. The system is also compatible with a broad range of fluid types, and is operable in magnetic resonance imaging environments. Performance of the system was validated using image processing-based analysis of piston motion as well as particle image velocimetry. The new system represents a more capable pumping solution for aortic flow experiments than other available designs, and can be manufactured at a relatively low cost.

Keywords

Piston pump Flow loop Pulsatile flow Blood flow Aorta Physiological waveform Heart valves 

References

  1. 1.
    Alwan, A. Global Status Report on Noncommunicable Diseases 2010. Geneva: World Health Organization, 2011.Google Scholar
  2. 2.
    Arbia, G., C. Corsini, M. E. Moghadam, A. L. Marsden, F. Migliavacca, G. Pennati, et al. Numerical blood flow simulation in surgical corrections: what do we need for an accurate analysis? J. Surg. Res.186(1):44–55, 2014.Google Scholar
  3. 3.
    Babiker, M. H., L. F. Gonzalez, J. Ryan, F. Albuquerque, D. Collins, A. Elvikis, et al. Influence of stent configuration on cerebral aneurysm fluid dynamics. J. Biomech. 45(3):440–447, 2012.Google Scholar
  4. 4.
    Chaudhury, R. A. Improved Techniques for Cardiovascular Flow Experiments. Tempe, AZ: Arizona State University, 2015.Google Scholar
  5. 5.
    Chaudhury, R. A., M. Herrmann, D. H. Frakes, and R. J. Adrian. Impact of development time on boundary conditions for constant volume flux start-up flow in arterial flow piston pumps. In: 7th World Congress of Biomechanics. Boston, MA, 2014.Google Scholar
  6. 6.
    Chaudhury, R. A., M. Herrmann, D. H. Frakes, and R. J. Adrian. Length and time for development of laminar flow in tubes following a step increase of volume flux. Exp. Fluids. 56(1):22–10, 2015.Google Scholar
  7. 7.
    Chaudhury, R. A., J. Ryan, D. H. Frakes, and R. J. Adrian. Prediction of downstream velocity waveforms for in vitro flow experiments. In: Summer Biomechanics, Bioengineering, and Biotransport Conference. Snowbird Resort, Utah, 2015.Google Scholar
  8. 8.
    Chung, B., and J. R Cebral. CFD for evaluation and treatment planning of aneurysms: review of proposed clinical uses and their challenges. Ann. Biomed. Eng. p. 1–17, 2015.Google Scholar
  9. 9.
    Crosby, J. R., K. J. DeCook, P. L. Tran, R. G. Smith, D. F. Larson, Z. I. Khalpey, et al. Physiological characterization of the SynCardia total artificial heart in a mock circulation system. ASAIO J. 61(3):274–281, 2015.Google Scholar
  10. 10.
    Donovan, F. Design of a hydraulic analog of the circulatory system for evaluating artificial hearts. Artif. Cells Blood Substit. Biotechnol. 3(4):439–449, 1975.Google Scholar
  11. 11.
    Dur, O., M. Yoshida, P. Manor, A. Mayfield, P. D. Wearden, V. O. Morell, et al. In vitro evaluation of right ventricular outflow tract reconstruction with bicuspid valved polytetrafluoroethylene conduit. Artif. Organs. 34(11):1010–1016, 2010.Google Scholar
  12. 12.
    Eriksson, A., H. W. Persson, and K. Lindström. A computer-controlled arbitrary flow wave form generator for physiological studies. Rev. Sci. Instrum. 71(1):235, 2000.Google Scholar
  13. 13.
    Frakes, D., C. Zwart, and W. Singhose. Extracting motion data from video using optical flow with physically-based constraints. Int. J. Control Autom. Syst. 11(1):48–57, 2013.Google Scholar
  14. 14.
    Frayne, R., and D. Holdsworth. Computer-controlled flow simulator for MR flow studies. J. Magn. Reson. Imaging. 2(5):605–612, 1992.Google Scholar
  15. 15.
    Harvard Apparatus. Series 1400 Pulsatile Blood Pumps User’s Manual. Holliston, MA, 2004.Google Scholar
  16. 16.
    Heidenreich, P.A., J. G. Trogdon, O. A. Khavjou, J. Butler, K. Dracup, M. D. Ezekowitz, et al. Forecasting the future of cardiovascular disease in the United States a policy statement from the American heart association. Circulation. 123(8):933–944, 2011.Google Scholar
  17. 17.
    Holdsworth, D. W., D. W. Rickey, M. Drangova, D. J. M. Miller, and A. Fenster. Computer-controlled positive displacement pump for physiological flow simulation. Med. Biol. Eng. Comput. 29(6):565–570, 1991.Google Scholar
  18. 18.
    Hoskins, P. R. Simulation and validation of arterial ultrasound imaging and blood flow. Ultrasound Med. Biol. 34(5):693–717, 2008.Google Scholar
  19. 19.
    Isselbacher, E. M. Thoracic and abdominal aortic aneurysms. Circulation 111(6):816–828, 2005.Google Scholar
  20. 20.
    Ku, D. Blood flow in arteries. Ann. Rev. Fluid Mech. 29(1):399–434, 1997.Google Scholar
  21. 21.
    Ku, J. P., C. J. Elkins, and C. A. Taylor. Comparison of CFD and MRI flow and velocities in an in vitro large artery bypass graft model. Ann. Biomed. Eng. 33(3):257–269, 2005.Google Scholar
  22. 22.
    Kung, E. O. In-vitro experimental validation of finite element analysis of blood flow and vessel wall dynamics. Stanford: Stanford University, 2010.Google Scholar
  23. 23.
    Kung, E. O., A. S. Les, C. A. Figueroa, F. Medina, K. Arcaute, R. B. Wicker, et al. In vitro validation of finite element analysis of blood flow in deformable models. Ann. Biomed. Eng. 39(7):1947–1960, 2011.Google Scholar
  24. 24.
    Kung, E. O., A. S. Les, F. Medina, R. B. Wicker, M. V. McConnell, and C. A. Taylor. In vitro validation of finite-element model of AAA hemodynamics incorporating realistic outlet boundary conditions. J. Biomech. Eng. 133(4):041003, 2011.Google Scholar
  25. 25.
    Kung, E. O., and C. A. Taylor. Development of a physical Windkessel module to re-create in vivo vascular flow impedance for in vitro experiments. Cardiovasc. Eng. Technol. 2(1):2–14, 2010.Google Scholar
  26. 26.
    Law, Y.F., R. S. Cobbold, K. W. Johnston, and P. A. Bascom. Computer-controlled pulsatile pump system for physiological flow simulation. Med. Biol. Eng. Comput. 25(5):590–595, 1987.Google Scholar
  27. 27.
    Lieber, B.B., V. Livescu, and L. N. Hopkins. Particle image velocimetry assessment of stent design influence on intra-aneurysmal flow. Ann. Biomed. Eng. 30(6):768–777, 2002.Google Scholar
  28. 28.
    Milnor, W. R. Hemodynamics. Baltimore: Williams & Wilkins, 1982.Google Scholar
  29. 29.
    Mozaffarian, D., E. J. Benjamin, A. S. Go, D. K. Arnett, M. J. Blaha, M. Cushman, et al. Heart disease and stroke statistics-2015 update: a report from the American Heart Association. Circulation. 2014.Google Scholar
  30. 30.
    Pahlevan, N. M., and M. Gharib. In-vitro investigation of a potential wave pumping effect in human aorta. J. Biomech. 46(13):2122–2129, 2013.Google Scholar
  31. 31.
    Shelley Medical Imaging Technologies. CardioFlow 5000 MR Computer-Controlled Flow Pump System. London, ON, 2015.Google Scholar
  32. 32.
    Taylor, C. E., Z. W. Dziczkowski, and G. E. Miller. Automation of the harvard apparatus pulsatile blood pump. J. Med. Devices. 6(4):045002, 2012.Google Scholar
  33. 33.
    The Medical Image Computing and Computer Assisted Intervention Society. CFD Challenge: Simulation of Hemodynamics in a Patient-Specific Aortic Coarctation Model, 2012. http://vascularmodel.org/miccai2012.
  34. 34.
    Timms, D., M. Hayne, K. McNeil, and A. Galbraith. A complete mock circulation loop for the evaluation of left, right, and biventricular assist devices. Artif. Organs. 29(7):564–572, 2005.Google Scholar
  35. 35.
    Tsai, W., and Ö. Savaş. Flow pumping system for physiological waveforms. Med. Biol. Eng. Comput. 48(2):197–201, 2010.Google Scholar
  36. 36.
    ViVitro Labs Inc., SuperPump System User Manual. Victoria, BC, 2014.Google Scholar
  37. 37.
    Xiang, J., A. Siddiqui, and H. Meng. The effect of inlet waveforms on computational hemodynamics of patient-specific intracranial aneurysms. J. Biomech. 47(16):3882–3890, 2014.Google Scholar
  38. 38.
    Yilmaz, S., O. Toker, N. Arslan, and H. Sedef. Optimal in vitro realization of pulsatile coronary artery flow waveforms using closed-loop feedback algorithms with multiple flow control devices. Turk. J. Electr. Eng. Comput. Sci. 20(6):1006–1030, 2012.Google Scholar
  39. 39.
    Yun, B. M., D. B. McElhinney, S. Arjunon, L. Mirabella, C..K. Aidun, and A. P. Yoganathan. Computational simulations of flow dynamics and blood damage through a bileaflet mechanical heart valve scaled to pediatric size and flow. J. Biomech. 47(12):3169–3177, 2014.Google Scholar

Copyright information

© Biomedical Engineering Society 2016

Authors and Affiliations

  1. 1.School of Biological and Health Systems EngineeringArizona State UniversityTempeUSA
  2. 2.School of Electrical, Computer, and Energy EngineeringArizona State UniversityTempeUSA
  3. 3.School for Engineering of Matter, Transport and EnergyArizona State UniversityTempeUSA

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