Skip to main content

Physical Simulators and Replicators in Endovascular Neurosurgery Training

  • Chapter
  • First Online:
Comprehensive Healthcare Simulation: Neurosurgery

Abstract

The increased adoption of endovascular neurosurgery procedures to treat cerebrovascular pathologies has led to the commercialization of a wide array of medical devices which, in turn, necessitates a more sophisticated training environment for physicians and fellows than the traditional “see one, do one, teach one” concept. Improvements in simulation technology and a changing healthcare culture are facilitating a wider assimilation of benchtop simulation models in lieu of cadaver or animal models in physician training as well as treatment planning. Medical device manufacturers as well as regulators are also increasingly utilizing such simulators for device development and assessment of efficacy. Low-fidelity physical simulacra in the form of simplistic vascular replicas with or without coarse pumping systems have been available for basic neuroendovascular simulations for several years. Additive manufacturing, or 3D printing, has ushered in the use of anatomically accurate vascular replicas derived from patient imaging. Other considerations that improve the fidelity of simulating the neuroendovascular compartment include flows and pressures, catheter friction, blood-analog fluid, X-ray attenuation, etc. This chapter briefly describes these components of high-fidelity physical simulators, called replicators, for endovascular neurosurgery training.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kunkler K. The role of medical simulation: an overview. Int J Med Rob Comput Assisted Surg. 2006;2(3):203–10. https://doi.org/10.1002/rcs.101.

    Article  Google Scholar 

  2. Rosen KR. The history of medical simulation. J Crit Care. 2008;23(2):157–66. https://doi.org/10.1016/j.jcrc.2007.12.004.

    Article  PubMed  Google Scholar 

  3. Bath J, Lawrence P. Why we need open simulation to train surgeons in an era of work-hour restrictions. Vascular. 2011;19(4):175–7. https://doi.org/10.1258/vasc.2011.oa0284.

    Article  PubMed  Google Scholar 

  4. Nesbitt CI, Birdi N, Mafeld S, Stansby G. The role of simulation in the development of endovascular surgical skills. Perspect Med Educ. 2016;5(1):8–14. https://doi.org/10.1007/s40037-015-0250-4.

    Article  PubMed Central  PubMed  Google Scholar 

  5. James JT. A new, evidence-based estimate of patient harms associated with hospital care. J Patient Saf. 2013;9(3):122–8. https://doi.org/10.1097/PTS.0b013e3182948a69.

    Article  PubMed  Google Scholar 

  6. Roguin A, Beyar R. Real case virtual reality training prior to carotid artery stenting. Catheter Cardiovasc Interv. 2010;75(2):279–82. https://doi.org/10.1002/ccd.22211.

    Article  PubMed  Google Scholar 

  7. Dawson DL. Training in carotid artery stenting: do carotid simulation systems really help? Vascular. 2006;14(5):256–63.

    Article  PubMed  Google Scholar 

  8. Tedesco MM, Pak JJ, Harris EJ, Jr., Krummel TM, Dalman RL, Lee JT. Simulation-based endovascular skills assessment: the future of credentialing? J Vasc Surg. 2008;47(5):1008–1; discussion 14. https://doi.org/10.1016/j.jvs.2008.01.007.

    Article  PubMed  Google Scholar 

  9. Lee JT, Qiu M, Teshome M, Raghavan SS, Tedesco MM, Dalman RL. The utility of endovascular simulation to improve technical performance and stimulate continued interest of preclinical medical students in vascular surgery. J Surg Educ. 2009;66(6):367–73. https://doi.org/10.1016/j.jsurg.2009.06.002.

    Article  PubMed  Google Scholar 

  10. Nestel D, Van Herzeele I, Aggarwal R, Odonoghue K, Choong A, Clough R, et al. Evaluating training for a simulated team in complex whole procedure simulations in the endovascular suite. Med Teach. 2009;31(1):e18–23. https://doi.org/10.1080/01421590802337104.

    Article  PubMed  Google Scholar 

  11. Aggarwal R, Mytton OT, Derbrew M, Hananel D, Heydenburg M, Issenberg B, et al. Training and simulation for patient safety. Qual Saf Health Care. 2010;19(Suppl 2):i34–43. https://doi.org/10.1136/qshc.2009.038562.

    Article  PubMed  Google Scholar 

  12. Boyle E, O’Keeffe DA, Naughton PA, Hill AD, McDonnell CO, Moneley D. The importance of expert feedback during endovascular simulator training. J Vasc Surg. 2011;54(1):240–8 e1. https://doi.org/10.1016/j.jvs.2011.01.058.

    Article  PubMed  Google Scholar 

  13. Willaert WI, Aggarwal R, Van Herzeele I, O’Donoghue K, Gaines PA, Darzi AW, et al. Patient-specific endovascular simulation influences interventionalists performing carotid artery stenting procedures. Eur J Vasc Endovasc Surg. 2011;41(4):492–500. https://doi.org/10.1016/j.ejvs.2010.12.013.

    Article  CAS  PubMed  Google Scholar 

  14. Eidt JF. The aviation model of vascular surgery education. J Vasc Surg. 2012;55(6):1801–9. https://doi.org/10.1016/j.jvs.2012.01.080.

    Article  PubMed  Google Scholar 

  15. Hseino H, Nugent E, Lee MJ, Hill AD, Neary P, Tierney S, et al. Skills transfer after proficiency-based simulation training in superficial femoral artery angioplasty. Simul Healthc. 2012;7(5):274–81. https://doi.org/10.1097/SIH.0b013e31825b6308.

    Article  PubMed  Google Scholar 

  16. Markovic J, Peyser C, Cavoores T, Fletcher E, Peterson D, Shortell C. Impact of endovascular simulator training on vascular surgery as a career choice in medical students. J Vasc Surg. 2012;55(5):1515–21. https://doi.org/10.1016/j.jvs.2011.11.060.

    Article  PubMed  Google Scholar 

  17. Duran C, Bismuth J, Mitchell E. A nationwide survey of vascular surgery trainees reveals trends in operative experience, confidence, and attitudes about simulation. J Vasc Surg. 2013;58(2):524–8. https://doi.org/10.1016/j.jvs.2012.12.072.

    Article  PubMed  Google Scholar 

  18. Robinson WP, 3rd, Schanzer A, Cutler BS, Baril DT, Larkin AC, Eslami MH, et al. A randomized comparison of a 3-week and 6-week vascular surgery simulation course on junior surgical residents’ performance of an end-to-side anastomosis. J Vasc Surg. 2012;56(6):1771–80; discussion 80–1. https://doi.org/10.1016/j.jvs.2012.06.105.

    Article  PubMed  Google Scholar 

  19. Lonn L, Edmond JJ, Marco J, Kearney PP, Gallagher AG. Virtual reality simulation training in a high-fidelity procedure suite: operator appraisal. J Vasc Interv Radiol. 2012;23(10):1361–6 e2. https://doi.org/10.1016/j.jvir.2012.06.002.

    Article  PubMed  Google Scholar 

  20. Eslahpazir BA, Goldstone J, Allemang MT, Wang JC, Kashyap VS. Principal considerations for the contemporary high-fidelity endovascular simulator design used in training and evaluation. J Vasc Surg. 2014;59(4):1154–62. https://doi.org/10.1016/j.jvs.2013.11.074.

    Article  PubMed  Google Scholar 

  21. Fargen KM, Siddiqui AH, Veznedaroglu E, Turner RD, Ringer AJ, Mocco J. Simulator based angiography education in neurosurgery: results of a pilot educational program. J Neurointerv Surg. 2012;4(6):438–41. https://doi.org/10.1136/neurintsurg-2011-010128.

    Article  PubMed  Google Scholar 

  22. Klein GT, Lu Y, Wang MY. 3D printing and neurosurgery – ready for prime time? World Neurosurg. 2013;80(3–4):233–5. https://doi.org/10.1016/j.wneu.2013.07.009.

    Article  PubMed  Google Scholar 

  23. Rengier F, Mehndiratta A, von Tengg-Kobligk H, Zechmann CM, Unterhinninghofen R, Kauczor HU, et al. 3D printing based on imaging data: review of medical applications. Int J Comput Assist Radiol Surg. 2010;5(4):335–41. https://doi.org/10.1007/s11548-010-0476-x.

    Article  CAS  PubMed  Google Scholar 

  24. Ventola CL. Medical applications for 3D printing: current and projected uses. P T. 2014;39(10):704–11.

    PubMed Central  PubMed  Google Scholar 

  25. Nute JL, Le Roux L, Chandler AG, Baladandayuthapani V, Schellingerhout D, Cody DD. Differentiation of low-attenuation intracranial hemorrhage and calcification using dual-energy computed tomography in a phantom system. Investig Radiol. 2015;50(1):9–16. https://doi.org/10.1097/rli.0000000000000089.

    Article  CAS  Google Scholar 

  26. Shmueli K, Thomas DL, Ordidge RJ. Design, construction and evaluation of an anthropomorphic head phantom with realistic susceptibility artifacts. J Magn Reson Imaging. 2007;26(1):202–7. https://doi.org/10.1002/jmri.20993.

    Article  PubMed  Google Scholar 

  27. Schwager K, Gilyoma JM. Ceramic model for temporal bone exercises – an alternative for human temporal bones? Laryngorhinootologie. 2003;82(10):683–6. https://doi.org/10.1055/s-2003-43242.

    Article  CAS  PubMed  Google Scholar 

  28. Suzuki M, Ogawa Y, Kawano A, Hagiwara A, Yamaguchi H, Ono H. Rapid prototyping of temporal bone for surgical training and medical education. Acta Otolaryngol. 2004;124(4):400–2.

    Article  PubMed  Google Scholar 

  29. Hurson C, Tansey A, O’Donnchadha B, Nicholson P, Rice J, McElwain J. Rapid prototyping in the assessment, classification and preoperative planning of acetabular fractures. Injury. 2007;38(10):1158–62. https://doi.org/10.1016/j.injury.2007.05.020.

    Article  CAS  PubMed  Google Scholar 

  30. Niikura T, Sugimoto M, Lee SY, Sakai Y, Nishida K, Kuroda R, et al. Tactile surgical navigation system for complex acetabular fracture surgery. Orthopedics. 2014;37(4):237–42. https://doi.org/10.3928/01477447-20140401-05.

    Article  PubMed  Google Scholar 

  31. Gallas RR, Hunemohr N, Runz A, Niebuhr NI, Jakel O, Greilich S. An anthropomorphic multimodality (CT/MRI) head phantom prototype for end-to-end tests in ion radiotherapy. Zeitschrift fur medizinische Physik. 2015;25:391–9. https://doi.org/10.1016/j.zemedi.2015.05.003.

    Article  PubMed  Google Scholar 

  32. Shikhaliev PM. Dedicated phantom materials for spectral radiography and CT. Phys Med Biol. 2012;57(6):1575–93. https://doi.org/10.1088/0031-9155/57/6/1575.

    Article  PubMed  Google Scholar 

  33. Mori K, Yamamoto T, Oyama K, Ueno H, Nakao Y, Honma K. Modified three-dimensional skull base model with artificial dura mater, cranial nerves, and venous sinuses for training in skull base surgery: technical note. Neurol Med Chir. 2008;48(12):582–7; discussion 7–8.

    Article  PubMed  Google Scholar 

  34. Wurm G, Lehner M, Tomancok B, Kleiser R, Nussbaumer K. Cerebrovascular biomodeling for aneurysm surgery: simulation-based training by means of rapid prototyping technologies. Surg Innov. 2011;18(3):294–306. https://doi.org/10.1177/1553350610395031.

    Article  PubMed  Google Scholar 

  35. Gatto M, Harris RA, Sama A, Watson J, editors. Investigating the effectiveness of three-dimensional-printing for producing realistic physical surgical training phantoms. Annals of DAAAM for 2010 and 21st International DAAAM Symposium “Intelligent Manufacturing and Automation: Focus on Interdisciplinary Solutions”, October 20, 2010 – October 23, 2010; 2010; Zadar: Danube Adria Association for Automation and Manufacturing, DAAAM.

    Google Scholar 

  36. Gatto M, Memoli G, Shaw A, Sadhoo N, Gelat P, Harris RA. Three-dimensional printing (3DP) of neonatal head phantom for ultrasound: thermocouple embedding and simulation of bone. Med Eng Phys. 2012;34(7):929–37. https://doi.org/10.1016/j.medengphy.2011.10.012.

    Article  PubMed  Google Scholar 

  37. Arcaute K, Wicker RB. Patient-specific compliant vessel manufacturing using dip-spin coating of rapid prototyped molds. J Manuf Sci E T ASME. 2008;130(5):0510081–05100813. https://doi.org/10.1115/1.2898839.

    Article  Google Scholar 

  38. Chueh JY, Wakhloo AK, Gounis MJ. Neurovascular modeling: small-batch manufacturing of silicone vascular replicas. AJNR Am J Neuroradiol. 2009;30(6):1159–64. https://doi.org/10.3174/ajnr.A1543.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Chaudhury RA, Atlasman V, Pathangey G, Pracht N, Adrian RJ, Frakes DH. A high performance pulsatile pump for aortic flow experiments in 3-dimensional models. Cardiovasc Eng Technol. 2016;7(2):148–58. https://doi.org/10.1007/s13239-016-0260-3.

    Article  PubMed  Google Scholar 

  40. Fung YC. Biomechanics : circulation. 2nd ed. New York: Springer; 1997.

    Book  Google Scholar 

  41. McDonald DA. Blood flow in arteries. 2nd ed. Baltimore: The Williams & Wilkins Company; 1974.

    Google Scholar 

  42. Lieber BB, Sadasivan C. Hemodynamics, Macrocirculatory. Encyclopedia of biomaterials and biomedical engineering. 2nd ed (Online Version). CRC Press; 2008. p. 1356–1367.

    Google Scholar 

  43. Segers P, Rietzschel ER, De Buyzere ML, Stergiopulos N, Westerhof N, Van Bortel LM, et al. Three- and four-element Windkessel models: assessment of their fitting performance in a large cohort of healthy middle-aged individuals. Proc Inst Mech Eng H J Eng Med. 2008;222(4):417–28.

    Article  CAS  Google Scholar 

  44. Kung EO, Taylor CA. Development of a physical windkessel module to re-create in-vivo vascular flow impedance for in-vitro experiments. Cardiovasc Eng Technol. 2011;2(1):2–14. https://doi.org/10.1007/s13239-010-0030-6.

    Article  PubMed  Google Scholar 

  45. O’Rourke MF, Hashimoto J. Mechanical factors in arterial aging: a clinical perspective. J Am Coll Cardiol. 2007;50(1):1–13. https://doi.org/10.1016/j.jacc.2006.12.050.

    Article  PubMed  Google Scholar 

  46. Volk MW. Pump characteristics and applications. Mechanical engineering, vol. 103. New York: M. Dekker; 1996.

    Google Scholar 

  47. Armignacco P, Garzotto F, Bellini C, Neri M, Lorenzin A, Sartori M, et al. Pumps in wearable ultrafiltration devices: pumps in wuf devices. Blood Purif. 2015;39(1–3):115–24. https://doi.org/10.1159/000368943.

    Article  PubMed  Google Scholar 

  48. Moazami N, Fukamachi K, Kobayashi M, Smedira NG, Hoercher KJ, Massiello A, et al. Axial and centrifugal continuous-flow rotary pumps: a translation from pump mechanics to clinical practice. J Heart Lung transplant. 2013;32(1):1–11. https://doi.org/10.1016/j.healun.2012.10.001.

    Article  PubMed  Google Scholar 

  49. Jahren SE, Ochsner G, Shu F, Amacher R, Antaki JF, Vandenberghe S. Analysis of pressure head-flow loops of pulsatile rotodynamic blood pumps. Artif Organs. 2014;38(4):316–26. https://doi.org/10.1111/aor.12139.

    Article  PubMed  Google Scholar 

  50. Pirbodaghi T, Axiak S, Weber A, Gempp T, Vandenberghe S. Pulsatile control of rotary blood pumps: does the modulation waveform matter? J Thorac Cardiovasc Surg. 2012;144(4):970–7. https://doi.org/10.1016/j.jtcvs.2012.02.015.

    Article  PubMed  Google Scholar 

  51. Cuenca-Navalon E, Laumen M, Finocchiaro T, Steinseifer U. Estimation of filling and afterload conditions by pump intrinsic parameters in a pulsatile total artificial heart. Artif Organs. 2016;40(7):638–44. https://doi.org/10.1111/aor.12636.

    Article  PubMed  Google Scholar 

  52. Gu YJ, van Oeveren W, Mungroop HE, Epema AH, den Hamer IJ, Keizer JJ, et al. Clinical effectiveness of centrifugal pump to produce pulsatile flow during cardiopulmonary bypass in patients undergoing cardiac surgery. Artif Organs. 2011;35(2):E18–26. https://doi.org/10.1111/j.1525-1594.2010.01152.x.

    Article  PubMed  Google Scholar 

  53. Sajgalik P, Grupper A, Edwards BS, Kushwaha SS, Stulak JM, Joyce DL, et al. Current status of left ventricular assist device therapy. Mayo Clin Proc. 2016;91(7):927–40. https://doi.org/10.1016/j.mayocp.2016.05.002.

    Article  PubMed  Google Scholar 

  54. Jungreis CA, Kerber CW. A solution that simulates whole blood in a model of the cerebral circulation. AJNR Am J Neuroradiol. 1991;12(2):329–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Anastasiou AD, Spyrogianni AS, Koskinas KC, Giannoglou GD, Paras SV. Experimental investigation of the flow of a blood analogue fluid in a replica of a bifurcated small artery. Med Eng Phys. 2012;34(2):211–8. https://doi.org/10.1016/j.medengphy.2011.07.012.

    Article  CAS  PubMed  Google Scholar 

  56. Yousif MY, Holdsworth DW, Poepping TL. Deriving a blood-mimicking fluid for particle image velocimetry in Sylgard-184 vascular models. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:1412–5. https://doi.org/10.1109/iembs.2009.5334175.

    Article  Google Scholar 

  57. Seong J, Sadasivan C, Onizuka M, Gounis MJ, Christian F, Miskolczi L, et al. Morphology of elastase-induced cerebral aneurysm model in rabbit and rapid prototyping of elastomeric transparent replicas. Biorheology. 2005;42(5):345–61.

    PubMed  Google Scholar 

  58. Santore J, Sadasivan C, Fiorella DJ, Lieber BB, Woo HH, editors. Creation of patient-specific silicone vascular replicas of aneurysm-parent vessel complexes. AANS/CNS Cerebrovascular Section Annual Meeting; 2012. Hilton New Orleans Riverside, New Orleans.

    Google Scholar 

  59. Kerber CW, Heilman CB. Flow dynamics in the human carotid artery: I. Preliminary observations using a transparent elastic model. AJNR Am J Neuroradiol. 1992;13(1):173–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Knox K, Kerber CW, Singel SA, Bailey MJ, Imbesi SG. Stereolithographic vascular replicas from CT scans: choosing treatment strategies, teaching, and research from live patient scan data. AJNR Am J Neuroradiol. 2005;26(6):1428–31.

    PubMed  PubMed Central  Google Scholar 

  61. Yagi T, Sato A, Shinke M, Takahashi S, Tobe Y, Takao H, et al. Experimental insights into flow impingement in cerebral aneurysm by stereoscopic particle image velocimetry: transition from a laminar regime. J R Soc Interface / R Soc. 2013;10(82):20121031. https://doi.org/10.1098/rsif.2012.1031.

    Article  Google Scholar 

  62. Anderson JR, Diaz O, Klucznik R, Zhang YJ, Britz GW, Grossman RG, et al. Validation of computational fluid dynamics methods with anatomically exact, 3D printed MRI phantoms and 4D pcMRI. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:6699–701. https://doi.org/10.1109/embc.2014.6945165.

    Article  Google Scholar 

  63. Khan IS, Kelly PD, Singer RJ. Prototyping of cerebral vasculature physical models. Surg Neurol Int. 2014;5:11. https://doi.org/10.4103/2152-7806.125858.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Russ M, O’Hara R, Setlur Nagesh SV, Mokin M, Jimenez C, Siddiqui A, et al. Treatment planning for image-guided neuro-vascular interventions using patient-specific 3D printed phantoms. Proceedings of SPIE – the International Society for Optical Engineering. 2015;9417. https://doi.org/10.1117/12.2081997.

  65. Ikeda S, Arai F, Fukuda T, Negoro M, Irie K. An in vitro patient-specific biological model of the cerebral artery reproduced with a membranous configuration for simulating endovascular intervention. J Rob Mechatronics. 2005;17(3):327–34.

    Article  Google Scholar 

  66. Mashiko T, Otani K, Kawano R, Konno T, Kaneko N, Ito Y, et al. Development of three-dimensional hollow elastic model for cerebral aneurysm clipping simulation enabling rapid and low cost prototyping. World Neurosurg. 2015;83(3):351–61. https://doi.org/10.1016/j.wneu.2013.10.032.

    Article  PubMed  Google Scholar 

  67. Wetzel SG, Ohta M, Handa A, Auer JM, Lylyk P, Lovblad KO, et al. From patient to model: stereolithographic modeling of the cerebral vasculature based on rotational angiography. AJNR Am J Neuroradiol. 2005;26(6):1425–7.

    PubMed  PubMed Central  Google Scholar 

  68. Yu CH, Ohta M, Kwon TK. Study of parameters for evaluating the pushability of interventional devices using box-shaped blood vessel biomodels made of PVA-H or silicone. Biomed Mater Eng. 2014;24(1):961–8. https://doi.org/10.3233/bme-130891.

    Article  CAS  PubMed  Google Scholar 

  69. Benet A, Plata-Bello J, Abla AA, Acevedo-Bolton G, Saloner D, Lawton MT. Implantation of 3D-printed patient-specific aneurysm models into cadaveric specimens: a new training paradigm to allow for improvements in cerebrovascular surgery and research. Biomed Res Int. 2015;2015:939387. https://doi.org/10.1155/2015/939387.

    Article  PubMed Central  PubMed  Google Scholar 

  70. Bassoli E, Gatto A, Iuliano L, Violante MG. 3D printing technique applied to rapid casting. Rapid Prototyp J. 2007;13(3):148–55. https://doi.org/10.1108/13552540710750898.

    Article  Google Scholar 

  71. Cheah CM, Chua CK, Lee CW, Feng C, Totong K. Rapid prototyping and tooling techniques: a review of applications for rapid investment casting. Int J Adv Manuf Technol. 2005;25(3–4):308–20. https://doi.org/10.1007/s00170-003-1840-6.

    Article  Google Scholar 

  72. Sachs E, Cima M, Williams P, Brancazio D, Cornie J. Three dimensional printing: rapid tooling and prototypes directly from a CAD model. J Eng Ind. 1992;114(4):481–8. https://doi.org/10.1115/1.2900701.

    Article  Google Scholar 

  73. Wicker R, Cortez M, Medina F, Palafox G, Elkins C, editors. Manufacturing of complex compliant cardiovascular system models for in-vitro hemodynamic experimentation using CT and MRI data and rapid prototyping technologies. ASME Summer Bioengineering Conference; 2001 Jun 27–Jul 1; Snowbird: ASME.

    Google Scholar 

  74. Arcaute K, Palafox GN, Medina F, Wicker RB, editors. Complex silicone aorta models manufactured using a dip-spin coating technique and water-soluble molds. 2003 Summer Bioengineering Conference; 2003 June 25–29; Key Biscayne.

    Google Scholar 

  75. Van Merode T, Hick PJ, Hoeks AP, Rahn KH, Reneman RS. Carotid artery wall properties in normotensive and borderline hypertensive subjects of various ages. Ultrasound Med Biol. 1988;14(7):563–9.

    Article  PubMed  Google Scholar 

  76. Hirai T, Sasayama S, Kawasaki T, Yagi S. Stiffness of systemic arteries in patients with myocardial infarction. A noninvasive method to predict severity of coronary atherosclerosis. Circulation. 1989;80(1):78–86.

    Article  CAS  PubMed  Google Scholar 

  77. Luc M, Polonsky T, Lammertin G, Spencer K. Automated border detection for assessing the mechanical properties of the carotid arteries: comparison with carotid intima-media thickness. J Am Soc Echocardiogr. 2010;23(5):567–72. https://doi.org/10.1016/j.echo.2010.01.024.

    Article  PubMed  Google Scholar 

  78. Kamenskiy AV, Dzenis YA, MacTaggart JN, Lynch TG, Jaffar Kazmi SA, Pipinos II. Nonlinear mechanical behavior of the human common, external, and internal carotid arteries in vivo. J Surg Res. 2012;176(1):329–36. https://doi.org/10.1016/j.jss.2011.09.058.

    Article  PubMed  Google Scholar 

  79. Hayashi K, Handa H, Nagasawa S, Okumura A, Moritake K. Stiffness and elastic behavior of human intracranial and extracranial arteries. J Biomech. 1980;13(2):175–84.

    Article  CAS  PubMed  Google Scholar 

  80. Franquet A, Avril S, Le Riche R, Badel P, Schneider FC, Boissier C, et al. Identification of the in vivo elastic properties of common carotid arteries from MRI: a study on subjects with and without atherosclerosis. J Mech Behav Biomed Mater. 2013;27:184–203. https://doi.org/10.1016/j.jmbbm.2013.03.016.

    Article  CAS  PubMed  Google Scholar 

  81. Tanaka H, Dinenno FA, Monahan KD, Clevenger CM, DeSouza CA, Seals DR. Aging, habitual exercise, and dynamic arterial compliance. Circulation. 2000;102(11):1270–5.

    Article  CAS  PubMed  Google Scholar 

  82. Hansen F, Mangell P, Sonesson B, Lanne T. Diameter and compliance in the human common carotid artery – variations with age and sex. Ultrasound Med Biol. 1995;21(1):1–9.

    Article  CAS  PubMed  Google Scholar 

  83. Lenard Z, Fulop D, Visontai Z, Jokkel G, Reneman R, Kollai M. Static versus dynamic distensibility of the carotid artery in humans. J Vasc Res. 2000;37(2):103–11. https://doi.org/10.1159/000025721.

    Article  CAS  PubMed  Google Scholar 

  84. Sato T, Sasaki T, Suzuki K, Matsumoto M, Kodama N, Hiraiwa K. Histological study of the normal vertebral artery – etiology of dissecting aneurysms. Neurol Med Chir. 2004;44(12):629–35; discussion 36.

    Article  PubMed  Google Scholar 

  85. Drangova M, Holdsworth DW, Boyd CJ, Dunmore PJ, Roach MR, Fenster A. Elasticity and geometry measurements of vascular specimens using a high-resolution laboratory CT scanner. Physiol Meas. 1993;14(3):277–90.

    Article  CAS  PubMed  Google Scholar 

  86. Dobrin PB. Mechanical properties of arteries. Physiol Rev. 1978;58(2):397–460.

    Article  CAS  PubMed  Google Scholar 

  87. Konig G, McAllister TN, Dusserre N, Garrido SA, Iyican C, Marini A, et al. Mechanical properties of completely autologous human tissue engineered blood vessels compared to human saphenous vein and mammary artery. Biomaterials. 2009;30(8):1542–50. https://doi.org/10.1016/j.biomaterials.2008.11.011.

    Article  CAS  PubMed  Google Scholar 

  88. L’Heureux N, Dusserre N, Konig G, Victor B, Keire P, Wight TN, et al. Human tissue-engineered blood vessels for adult arterial revascularization. Nat Med. 2006;12(3):361–5. https://doi.org/10.1038/nm1364.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  89. Monson KL, Goldsmith W, Barbaro NM, Manley GT. Axial mechanical properties of fresh human cerebral blood vessels. J Biomech Eng. 2003;125(2):288–94.

    Article  PubMed  Google Scholar 

  90. Sarkar S, Salacinski HJ, Hamilton G, Seifalian AM. The mechanical properties of infrainguinal vascular bypass grafts: their role in influencing patency. Eur J Vasc Endovasc Surg. 2006;31(6):627–36. https://doi.org/10.1016/j.ejvs.2006.01.006.

    Article  CAS  PubMed  Google Scholar 

  91. Kazmierska K, Szwast M, Ciach T. Determination of urethral catheter surface lubricity. J Mater Sci Mater Med. 2008;19(6):2301–6. https://doi.org/10.1007/s10856-007-3339-4.

    Article  CAS  PubMed  Google Scholar 

  92. Caldwell RA, Woodell JE, Ho SP, Shalaby SW, Boland T, Langan EM, et al. In vitro evaluation of phosphonylated low-density polyethylene for vascular applications. J Biomed Mater Res. 2002;62(4):514–24. https://doi.org/10.1002/jbm.10249.

    Article  CAS  PubMed  Google Scholar 

  93. Ohta M, Handa A, Iwata H, Rufenacht DA, Tsutsumi S. Poly-vinyl alcohol hydrogel vascular models for in vitro aneurysm simulations: the key to low friction surfaces. Technol Health Care. 2004;12(3):225–33.

    PubMed  Google Scholar 

  94. Persson BNJ. Silicone rubber adhesion and sliding friction. Tribol Lett. 2016;62(2). https://doi.org/10.1007/s11249-016-0680-0.

  95. Kosukegawa H, Mamada K, Kuroki K, Liu L, Inoue K, Hayase T, et al. Measurements of dynamic viscoelasticity of poly (vinyl alcohol) hydrogel for the development of blood vessel biomodeling. J Fluid Sci Tech. 2008;3(4):533–43. https://doi.org/10.1299/jfst.3.533.

    Article  Google Scholar 

  96. Kosukegawa H, Shida S, Hashida Y, Ohta M, editors. Mechanical properties of tube-shaped poly (vinyl alcohol) hydrogel blood vessel biomodel. ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting; 2010. Montreal: Fluids Engineering Division.

    Google Scholar 

  97. Yu C, Kosukegawa H, Mamada K, Kuroki K, Takashima K, Yoshinaka K, et al. Development of an in vitro tracking system with poly (vinyl alcohol) hydrogel for catheter motion. J Biomech Sci Eng. 2010;5(1):11–7. https://doi.org/10.1299/jbse.5.11.

    Article  Google Scholar 

  98. Kosukegawa H, Kiyomitsu C, Ohta M, editors. Control of wall thickness of blood vessel biomodel made of poly (vinyl alcohol) hydrogel by a three-dimensional-rotating spin dip-coating method. ASME 2011 International Mechanical Engineering Congress and Exposition; 2011. Denver: ASME.

    Google Scholar 

  99. Mutoh T, Ishikawa T, Ono H, Yasui N. A new polyvinyl alcohol hydrogel vascular model (KEZLEX) for microvascular anastomosis training. Surg Neurol Int. 2010;1:74. https://doi.org/10.4103/2152-7806.72626.

    Article  PubMed Central  PubMed  Google Scholar 

  100. Spetzger U, von Schilling A, Brombach T, Winkler G. Training models for vascular microneurosurgery. Acta Neurochir Suppl. 2011;112:115–9. https://doi.org/10.1007/978-3-7091-0661-7_21.

    Article  PubMed  Google Scholar 

  101. Saboori P, Sadegh A. Material modeling of the head’s subarachnoid space. Sci Iran. 2011;18(6):1492–9.

    Article  Google Scholar 

  102. Fallenstein GT, Hulce VD, Melvin JW. Dynamic mechanical properties of human brain tissue. J Biomech. 1969;2(3):217–26.

    Article  CAS  PubMed  Google Scholar 

  103. Cotter CS, Smolarkiewicz PK, Szczyrba IN. A viscoelastic fluid model for brain injuries. Int J Numer Methods Fluids. 2002;40(1–2):303–11. https://doi.org/10.1002/fld.287.

    Article  Google Scholar 

  104. Chen X, Lam YM. Technical note: CT determination of the mineral density of dry bone specimens using the dipotassium phosphate phantom. Am J Phys Anthropol. 1997;103(4):557–60. https://doi.org/10.1002/(sici)1096-8644(199708)103:4<557::aid-ajpa10>3.0.co;2-#.

    Article  CAS  PubMed  Google Scholar 

  105. Giambini H, Dragomir-Daescu D, Huddleston PM, Camp JJ, An KN, Nassr A. The effect of quantitative computed tomography acquisition protocols on bone mineral density estimation. J Biomech Eng. 2015;137(11). https://doi.org/10.1115/1.4031572.

    Article  PubMed  Google Scholar 

  106. Mah P, Reeves TE, McDavid WD. Deriving Hounsfield units using grey levels in cone beam computed tomography. Dentomaxillofac Radiol. 2010;39(6):323–35. https://doi.org/10.1259/dmfr/19603304.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  107. Delye H, Clijmans T, Mommaerts MY, Sloten JV, Goffin J. Creating a normative database of age-specific 3D geometrical data, bone density, and bone thickness of the developing skull: a pilot study. J Neurosurg Pediatr. 2015;16(6):687–702. https://doi.org/10.3171/2015.4.peds1493.

    Article  PubMed  Google Scholar 

  108. Lee S, Chung CK, Oh SH, Park SB. Correlation between bone mineral density measured by dual-energy X-ray absorptiometry and Hounsfield units measured by diagnostic CT in lumbar spine. J Korean Neurosurg Soc. 2013;54(5):384–9. https://doi.org/10.3340/jkns.2013.54.5.384.

    Article  PubMed Central  PubMed  Google Scholar 

  109. Kowalczyk M, Wall A, Turek T, Kulej M, Scigala K, Kawecki J. Computerized tomography evaluation of cortical bone properties in the tibia. Ortop Traumatol Rehabil. 2007;9(2):187–97.

    PubMed  Google Scholar 

  110. Turkyilmaz I, Ozan O, Yilmaz B, Ersoy AE. Determination of bone quality of 372 implant recipient sites using Hounsfield unit from computerized tomography: a clinical study. Clin Implant Dent Relat Res. 2008;10(4):238–44. https://doi.org/10.1111/j.1708-8208.2008.00085.x.

    Article  PubMed  Google Scholar 

  111. Lim Fat D, Kennedy J, Galvin R, O’Brien F, Mc Grath F, Mullett H. The Hounsfield value for cortical bone geometry in the proximal humerus – an in vitro study. Skelet Radiol. 2012;41(5):557–68. https://doi.org/10.1007/s00256-011-1255-7.

    Article  Google Scholar 

  112. Schreiber JJ, Anderson PA, Hsu WK. Use of computed tomography for assessing bone mineral density. Neurosurg Focus. 2014;37(1):E4. https://doi.org/10.3171/2014.5.focus1483.

    Article  PubMed  Google Scholar 

  113. Cassetta M, Stefanelli LV, Pacifici A, Pacifici L, Barbato E. How accurate is CBCT in measuring bone density? A comparative CBCT-CT in vitro study. Clin Implant Dent Relat Res. 2014;16(4):471–8. https://doi.org/10.1111/cid.12027.

    Article  PubMed  Google Scholar 

  114. Peterson J, Dechow PC. Material properties of the human cranial vault and zygoma. Anat Rec A Discov Mol Cell Evol Biol. 2003;274(1):785–97. https://doi.org/10.1002/ar.a.10096.

    Article  PubMed  Google Scholar 

  115. Delye H, Verschueren P, Depreitere B, Verpoest I, Berckmans D, Vander Sloten J, et al. Biomechanics of frontal skull fracture. J Neurotrauma. 2007;24(10):1576–86. https://doi.org/10.1089/neu.2007.0283.

    Article  PubMed  Google Scholar 

  116. Adeloye A, Kattan KR, Silverman FN. Thickness of the normal skull in the American blacks and whites. Am J Phys Anthropol. 1975;43(1):23–30. https://doi.org/10.1002/ajpa.1330430105.

    Article  CAS  PubMed  Google Scholar 

  117. Lynnerup N, Astrup JG, Sejrsen B. Thickness of the human cranial diploe in relation to age, sex and general body build. Head Face Med. 2005;1:13. https://doi.org/10.1186/1746-160x-1-13.

    Article  PubMed Central  PubMed  Google Scholar 

  118. Lynnerup N. Cranial thickness in relation to age, sex and general body build in a Danish forensic sample. Forensic Sci Int. 2001;117(1–2):45–51.

    Article  CAS  PubMed  Google Scholar 

  119. Sabanciogullari V, Salk I, Cimen M. The relationship between total calvarial thickness and diploe in the elderly. Int J Morphol. 2013;31(1):38–44.

    Article  Google Scholar 

  120. Kobler JP, Prielozny L, Lexow GJ, Rau TS, Majdani O, Ortmaier T. Mechanical characterization of bone anchors used with a bone-attached, parallel robot for skull surgery. Med Eng Phys. 2015;37(5):460–8. https://doi.org/10.1016/j.medengphy.2015.02.012.

    Article  PubMed  Google Scholar 

  121. Park HK, Dujovny M, Agner C, Diaz FG. Biomechanical properties of calvarium prosthesis. Neurol Res. 2001;23(2–3):267–76. https://doi.org/10.1179/016164101101198424.

    Article  CAS  PubMed  Google Scholar 

  122. Zysset PK, Guo XE, Hoffler CE, Moore KE, Goldstein SA. Elastic modulus and hardness of cortical and trabecular bone lamellae measured by nanoindentation in the human femur. J Biomech. 1999;32(10):1005–12.

    Article  CAS  PubMed  Google Scholar 

  123. Dechow PC, Nail GA, Schwartz-Dabney CL, Ashman RB. Elastic properties of human supraorbital and mandibular bone. Am J Phys Anthropol. 1993;90(3):291–306. https://doi.org/10.1002/ajpa.1330900304.

    Article  CAS  PubMed  Google Scholar 

  124. Keaveny TM, Hayes WC. Mechanical properties of cortical and trabecular bone. In: Hall BK, editor. Bone: Bone growth – B. Boca Raton: CRC Press; 1993. p. 285–344.

    Google Scholar 

  125. Reilly DT, Burstein AH. The elastic and ultimate properties of compact bone tissue. J Biomech. 1975;8(6):393–405.

    Article  CAS  PubMed  Google Scholar 

  126. Camargo ECS, González G, González RG, Lev MH. Unenhanced computed tomography. In: González RG, Hirsch JA, Koroshetz WJ, Lev MH, Schaefer PW, editors. Acute ischemic stroke: imaging and intervention. Berlin, Heidelberg: Springer Berlin Heidelberg; 2006. p. 41–56.

    Chapter  Google Scholar 

Download references

Acknowledgment

We thank Brandon Kovarovic for his assistance in writing portions of the Flow Pumps section.

Conflict of Interest

All authors have a significant financial interest in Vascular Simulations, LLC.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chander Sadasivan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sadasivan, C., Lieber, B.B., Woo, H.H. (2018). Physical Simulators and Replicators in Endovascular Neurosurgery Training. In: Alaraj, A. (eds) Comprehensive Healthcare Simulation: Neurosurgery. Comprehensive Healthcare Simulation. Springer, Cham. https://doi.org/10.1007/978-3-319-75583-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75583-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75582-3

  • Online ISBN: 978-3-319-75583-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics