Annals of Biomedical Engineering

, Volume 45, Issue 3, pp 525–541 | Cite as

SimVascular: An Open Source Pipeline for Cardiovascular Simulation

  • Adam Updegrove
  • Nathan M. Wilson
  • Jameson Merkow
  • Hongzhi Lan
  • Alison L. Marsden
  • Shawn C. Shadden
Article

Abstract

Patient-specific cardiovascular simulation has become a paradigm in cardiovascular research and is emerging as a powerful tool in basic, translational and clinical research. In this paper we discuss the recent development of a fully open-source SimVascular software package, which provides a complete pipeline from medical image data segmentation to patient-specific blood flow simulation and analysis. This package serves as a research tool for cardiovascular modeling and simulation, and has contributed to numerous advances in personalized medicine, surgical planning and medical device design. The SimVascular software has recently been refactored and expanded to enhance functionality, usability, efficiency and accuracy of image-based patient-specific modeling tools. Moreover, SimVascular previously required several licensed components that hindered new user adoption and code management and our recent developments have replaced these commercial components to create a fully open source pipeline. These developments foster advances in cardiovascular modeling research, increased collaboration, standardization of methods, and a growing developer community.

Keywords

Patient-specific modeling Image-based CFD Hemodynamics Open-source 

References

  1. 1.
    Arzani, A., P. Dyverfeldt, T. Ebbers, and S. C. Shadden (2012) In vivo validation of numerical prediction for turbulence intensity in an aortic coarctation. Ann. Biomed. Eng. 40(4):860–870.CrossRefPubMedGoogle Scholar
  2. 2.
    Arzani, A., A. M. Gambaruto, G. Chen, and S. C. Shadden. Lagrangian wall shear stress structures and near wall transport in high schmidt aneurysmal flows. J. Fluid Mech. 790:158–172, 2016.CrossRefGoogle Scholar
  3. 3.
    Arzani, A., A. S Les, R. L. Dalman, and S. C. Shadden. Effect of exercise on patient specific abdominal aortic aneurysm flow topology and mixing. Int. J. Numer. Methods Biomed. Eng. 30(2):280–295, 2014.CrossRefGoogle Scholar
  4. 4.
    Arzani A., and S. C. Shadden. Characterization of the transport topology in patient-specific abdominal aortic aneurysm models. Phys. Fluids (1994-present) (1994), 24(8):081901, 2012.CrossRefGoogle Scholar
  5. 5.
    Arzani, A., G. Y. Suh, R. L. Dalman, and S. C. Shadden. A longitudinal comparison of hemodynamics and intraluminal thrombus deposition in abdominal aortic aneurysms. Am. J. Physiol. Heart Circ. Physiol. 307(12):H1786–H1795, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Astorino, M., J. Hamers, S. C. Shadden, and J. Gerbeau. A robust and efficient valve model based on resistive immersed surfaces. Int. J. Numer. Methods Biomed. Eng. 28(9):937–959, 2012.CrossRefGoogle Scholar
  7. 7.
    Bezdek, J. C., L. O. Hall, and L. P. Clarke. Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20(4):1033–1048, 1992.CrossRefGoogle Scholar
  8. 8.
    Bockman, M. D., A. P. Kansagra, S. C. Shadden, E. C. Wong, and A. L. Marsden. Fluid mechanics of mixing in the vertebrobasilar system: Cardiovasc. Eng. Technol. 3(4):450–461, 2012.CrossRefGoogle Scholar
  9. 9.
    Bogren, H. G., R. H. Klipstein, D. N. Firmin, R. H. Mohiaddin, S. R. Underwood, R. S. O. Rees, and D. B. Longmore. Quantitation of antegrade and retrograde blood flow in the human aorta by magnetic resonance velocity mapping. Am. Heart J. 117(6):1214–1222, 1989.CrossRefPubMedGoogle Scholar
  10. 10.
    Braunwald, E., E. M. Antman, J. W. Beasley, R. M. Califf, M. D. Cheitlin, J. S. Hochman, R. H. Jones, D. Kereiakes, J. Kupersmith, T. N. Levin, et al. Acc/aha 2002 guideline update for the management of patients with unstable angina and non-st-segment elevation myocardial infarction–summary article: a report of the american college of cardiology/american heart association task force on practice guidelines (committee on the management of patients with unstable angina). J. Am. Coll. Cardiol. 40(7):1366–1374, 2002.CrossRefPubMedGoogle Scholar
  11. 11.
    Carr, I. A., N. Nemoto, R. S. Schwartz, and S. C. Shadden. Size-dependent predilections of cardiogenic embolic transport. Am. J. Physiol. Heart Circ. Physiol. 305(5):H732–H739, 2013.CrossRefPubMedGoogle Scholar
  12. 12.
    Cheng, C. P., R. J. Herfkens, C. A. Taylor, and J. A. Feinstein. Proximal pulmonary artery blood flow characteristics in healthy subjects measured in an upright posture using MRI: the effects of exercise and age. J. Magn. Resonance Imaging 21(6):752–758, 2005.CrossRefGoogle Scholar
  13. 13.
    Coogan, J. S., J. D. Humphrey, and C. A. Figueroa. Computational simulations of hemodynamic changes within thoracic, coronary, and cerebral arteries following early wall remodeling in response to distal aortic coarctation. Biomech. Model. Mechanobiol. 12(1):79–93, 2013.CrossRefPubMedGoogle Scholar
  14. 14.
    Davies, P. F., M. Civelek, Y. Fang, and I. Fleming. The atherosusceptible endothelium: endothelial phenotypes in complex haemodynamic shear stress regions in vivo. Cardiovasc. Res. 99(2):315–327, 2013.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Esmaily-Moghadam, M., Y. Bazilevs, T.-Y. Hsia, I. E. Vignon-Clementel, and A. L. Marsden. A comparison of outlet boundary treatments for prevention of backflow divergence with relevance to blood flow simulations. Comput. Mech. 48:277–291, 2011.CrossRefGoogle Scholar
  16. 16.
    Esmaily-Moghadam, M., Y. Bazilevs, and A. L. Marsden. A new preconditioning technique for implicitly coupled multidomain simulations with applications to hemodynamics. Comput. Mech. 52:1141–1152, 2013.CrossRefGoogle Scholar
  17. 17.
    Esmaily-Moghadam, M., T.-Y. Hsia, and A. L. Marsden. The Assisted Bidirectional Glenn: a novel surgical approach for first stage single ventricle heart palliation. J. Thorac. Cardiovasc. Surg. 149(3):699–705, 2015.CrossRefPubMedGoogle Scholar
  18. 18.
    Esmaily-Moghadam, M., I. E. Vignon-Clementel, R. Figliola, and A. L. Marsden. A modular numerical method for implicit 0D/3D coupling in cardiovascular finite element simulations. J. Comput. Phys. 244:63–79, 2013.CrossRefGoogle Scholar
  19. 19.
    Figueroa, C. A., I. E. Vignon-Clementel, K. E. Jansen, T. J.R. Hughes, and C. A. Taylor. A coupled momentum method for modeling blood flow in three-dimensional deformable arteries. Comput. Methods Appl. Mech. Eng. 195:5685–5706, 2006.CrossRefGoogle Scholar
  20. 20.
    L. P. Franca and S. L. Frey. Stabilized finite element methods: II. The incompressible navier-stokes equations. Comput. Methods Appl. Mech. Eng. 99(2–3):209–233, 1992.CrossRefGoogle Scholar
  21. 21.
    Hansen K. B., and S. C. Shadden. A reduced-dimensional model for near-wall transport in cardiovascular flows. Biomech. Model. Mechanobiol. 15(3):713–722, 2016.CrossRefPubMedGoogle Scholar
  22. 22.
    Kim, H. J., I. E. Vignon-Clementel, J. S. Coogan, C. A. Figueroa, K. E. Jansen, and C.A. Taylor. Patient-specific modeling of blood flow and pressure in human coronary arteries. Ann. Biomed. Eng. 38(10):3195–3209, 2010.CrossRefPubMedGoogle Scholar
  23. 23.
    Krams, R., J. J. Wentzel, J. A.F. Oomen, R. Vinke, J. C.H. Schuurbiers, P. J. De Feyter, P. W. Serruys, and C. J. Slager. Evaluation of endothelial shear stress and 3D geometry as factors determining the development of atherosclerosis and remodeling in human coronary arteries in vivo Combining 3D reconstruction from angiography and IVUS (ANGUS) with computational fluid dynamics. Arterioscler. Thromb. Vasc. Biol. 17(10):2061–2065, 1997.CrossRefPubMedGoogle Scholar
  24. 24.
    Ku, D. N., D. P. Giddens, C. K. Zarins, and S. Glagov. Pulsatile flow and atherosclerosis in the human carotid bifurcation. positive correlation between plaque location and low oscillating shear stress. Arterioscler. Thromb. Vasc. Biol. 5(3):293–302, 1985.CrossRefGoogle Scholar
  25. 25.
    Kung, E. O., A. Baretta, C. Baker, G. Arbia, G. Biglino, C. Corsini, S. Schievano, I. E. Vignon-Clementel, G. Dubini, G. Pennati, et al. Predictive modeling of the virtual Hemi-Fontan operation for second stage single ventricle palliation: two patient-specific cases. J. Biomech. 46(2):423–429, 2013.CrossRefPubMedGoogle Scholar
  26. 26.
    Kung, E. O., A. M. Kahn, J. C. Burns, and A. L. Marsden. In vitro validation of patient-specific hemodynamic simulations in coronary aneurysms caused by Kawasaki disease. Cardiovasc. Eng. Technol. 5(2):189–201, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Kung, E. O., A. S. Les, C. A. Figueroa, F. Medina, K. Arcaute, R. B. Wicker, M. V. McConnell, and C. A. Taylor. In vitro validation of finite element analysis of blood flow in deformable models. Ann. Biomed. Eng. 39(7):1947–1960, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Kwak, B. R., M. Bäck, M.-L. Bochaton-Piallat, G. Caligiuri, M. J.A.P. Daemen, P. F. Davies, I. E. Hoefer, P. Holvoet, H. Jo, R. Krams, et al. Biomechanical factors in atherosclerosis: mechanisms and clinical implications. Eur. Heart J. 35(43):3013-3020, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Les, A. S., S. C. Shadden, C. A. Figueroa, J. M. Park, M. M. Tedesco, R. J. Herfkens, R. L. Dalman, and C. A. Taylor. Quantification of Hemodynamics in Abdominal Aortic Aneurysms During Rest and Exercise Using Magnetic Resonance Imaging and Computational Fluid DynamicsQuantification of hemodynamics in abdominal aortic aneurysms during rest and exercise using magnetic resonance imaging and computational fluid dynamics. Ann. Biomed. Eng. 38(4):1288–1313, 2010.CrossRefPubMedGoogle Scholar
  30. 30.
    Li, C., R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore. A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri. IEEE Trans. Image Process. 20(7):2007–2016, 2011.CrossRefPubMedGoogle Scholar
  31. 31.
    Lonyai, A., A. M. Dubin, J. A. Feinstein, C. A. Taylor, and S. C. Shadden. New insights into pacemaker lead-induced venous occlusion: Simulation-based investigation of alterations in venous biomechanics. Cardiovasc. Eng. 10(2):84–90, 2010.CrossRefPubMedGoogle Scholar
  32. 32.
    Lorigo, L. M., O. D. Faugeras, W. E. L. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C.-F. Westin. Curves: Curve evolution for vessel segmentation. Med. Image Anal. 5(3):195–206, 2001.CrossRefPubMedGoogle Scholar
  33. 33.
    Malek, A. M., S. L. Alper, and S. Izumo. Hemodynamic shear stress and its role in atherosclerosis. JAMA 282(21):2035–2042, 1999.CrossRefPubMedGoogle Scholar
  34. 34.
    Marsden, A. L., A. J. Bernstein, V. M. Reddy, S. C. Shadden, R. L. Spilker, F. P. Chan, C. A. Taylor, and J. A. Feinstein. Evaluation of a novel Y-shaped extracardiac Fontan baffle using computational fluid dynamics. J. Thorac. Cardiovasc. Surg. 137(2):394–U187, 2009.CrossRefGoogle Scholar
  35. 35.
    Marsden A. L., and M. Esmaily-Moghadam. Multiscale modeling of cardiovascular flows for clinical decision support. Appl. Mech. Rev. 67(3):030804, 2015.CrossRefGoogle Scholar
  36. 36.
    Marsden, A. L., V. M. Reddy, S. C. Shadden, F. P. Chan, C. A. Taylor, and J. A. Feinstein. A new multiparameter approach to computational simulation for Fontan assessment and redesign. Congenit. Heart Dis. 5(2):104–117, 2010.CrossRefPubMedGoogle Scholar
  37. 37.
    Martin, M. H., J. A. Feinstein, F. P. Chan, A. L. Marsden, W. Yang, and V. M. Reddy. Technical feasibility and intermediate outcomes of a hand-crafted, area-preserving, bifurcated “Y-Graft” Fontan. Thorac. Cardiovasc. Surg. 149(1):247–255, 2015.CrossRefGoogle Scholar
  38. 38.
    Merkow, J., Z. Tu, D. Kriegman, and A. L. Marsden. Structural edge detection for cardiovascular modeling. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 735–742. Springer, New York, 2015.Google Scholar
  39. 39.
    Milner, J. S., J. A. Moore, B. K. Rutt, and D. A. Steinman. Hemodynamics of human carotid artery bifurcations: computational studies with models reconstructed from magnetic resonance imaging of normal subjects. J. Vasc. Surg. 28(1):143–156, 1998.CrossRefPubMedGoogle Scholar
  40. 40.
    Moore, J. A., D. A. Steinman, D. W. Holdsworth, and C. R. Ethier. Accuracy of computational hemodynamics in complex arterial geometries reconstructed from magnetic resonance imaging. Ann. Viomedical Eng. 27(1):32–41, 1999.CrossRefGoogle Scholar
  41. 41.
    Morbiducci, U., A. M. Kok, B. R. Kwak, P. H. Stone, D. A. Steinman, J. J. Wentzel, et al. Atherosclerosis at arterial bifurcations: evidence for the role of haemodynamics and geometry. Thromb. Haemost. 115(3):484–492, 2016.CrossRefPubMedGoogle Scholar
  42. 42.
    Morgan, V. L., R. J. Roselli, and C. H. Lorenz. Normal three-dimensional pulmonary artery flow determined by phase contrast magnetic resonance imaging. Ann. Biomed. Eng. 26(4):557–566, 1998.CrossRefPubMedGoogle Scholar
  43. 43.
    Mukherjee, D., N.D. Jani, K. Selvaganesan, C.L. Weng, and S.C. Shadden. Computational assessment of the relation between embolism source and embolus distribution to the circle of Willis for improved understanding of stroke etiology. J. Biomech. Eng. 138(8):081008-1–081008-13, 2016.CrossRefGoogle Scholar
  44. 44.
    Mukherjee, D., J. Padilla, and S. C. Shadden. Numerical investigation of fluid-particle interactions for embolic stroke. Theor. Comput. Fluid Dyn. 30(1):23–39, 2016.CrossRefGoogle Scholar
  45. 45.
    Nichols, W., M. O’Rourke, and C. Vlachopoulos. McDonald’s Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles. CRC Press, Boca Raton, 2011.Google Scholar
  46. 46.
    Oakes, J. M., A. L. Marsden, C. Grandmont, S. C. Shadden, C. Darquenne, and I. E. Vignon-Clementel. Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments. Ann. Biomed. Eng. 42(4):899–914, 2014.CrossRefPubMedGoogle Scholar
  47. 47.
    Olufsen, M. S., C. S. Peskin, W. Y. Kim, E. M. Pedersen, A. Nadim, and J. Larsen. Numerical simulation and experimental validation of blood flow in arteries with structured-tree outflow conditions. Ann. Biomed. Eng. 28(11):1281–1299, 2000.CrossRefPubMedGoogle Scholar
  48. 48.
    Perktold, K., M. Hofer, G. Karner, W. Trubel, and H. Schima. Computer simulation of vascular fluid dynamics and mass transport: optimal design of arterial bypass anastomoses. Proc. ECCOMAS 98:484–489, 1998.Google Scholar
  49. 49.
    Ramachandra, A. B., A. M. Kahn, and A. L. Marsden. Patient-specific simulations reveal significant differences in mechanical stimuli in venous and arterial coronary grafts. J. Cardiovasc. Transl. Res. 9(4):279–290, 2016.CrossRefPubMedGoogle Scholar
  50. 50.
    Roccabianca, S., C.A. Figueroa, G. Tellides, and J.D. Humphrey. Quantification of regional differences in aortic stiffness in the aging human. J. Mech. Behav. Biomed. Mater. 29:618–634, 2014.CrossRefPubMedGoogle Scholar
  51. 51.
    Sahni, O., J. Müller, K. E. Jansen, M. S. Shephard, and C. A. Taylor. Efficient anisotropic adaptive discretization of the cardiovascular system. Comput. Methods Appl. Mech. Eng. 195(41–43):5634–5655, August 2006.CrossRefGoogle Scholar
  52. 52.
    Sankaran, S., M. Esmaily-Moghadam, A. M. Kahn, J. Guccione, E. Tseng, and A. L. Marsden. Patient-specific multiscale modeling of blood flow for coronary artery bypass graft surgery. Ann. Biomed. Eng. 40(1):2228–2242, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Schiavazzi, D. E., G. Arbia, C. Baker, A. M. Hlavacek, T. Y. Hsia, A. L. Marsden, I. E. Vignon-Clementel, and The Modeling of Congenital Hearts Alliance (MOCHA) Investigators. Uncertainty quantification in virtual surgery hemodynamics predictions for single ventricle palliation. Int. J. Numer. Methods Biomed. Eng. 32(3), 2016.CrossRefGoogle Scholar
  54. 54.
    Schiavazzi, D. E., E. O. Kung, A. L. Marsden, C. Baker, G. Pennati, T.-Y. Hsia, A. Hlavacek, A. L. Dorfman and Modeling of Congenital Hearts Alliance (MOCHA) Investigators et al. Hemodynamic effects of left pulmonary artery stenosis after superior cavopulmonary connection: a patient-specific multiscale modeling study. J. Thorac. Cardiovasc. Surg. 149(3):689–696, 2015.CrossRefPubMedGoogle Scholar
  55. 55.
    Shadden S. C., and C. A. Taylor. Characterization of coherent structures in the cardiovascular system. Ann. Biomed. Eng. 36:1152–1162, 2008.CrossRefPubMedGoogle Scholar
  56. 56.
    Steinman, D. A., Y. Hoi, P. Fahy, L. Morris, M. T. Walsh, N. Aristokleous, A. S. Anayiotos, Y. Papaharilaou, A. Arzani, S. C. Shadden, et al. Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge. J. Biomech. Eng. 135(2):021016, 2013.CrossRefPubMedGoogle Scholar
  57. 57.
    Suh, G. Y., A. S. Les, A. S. Tenforde, S. C. Shadden, R. L. Spilker, J. J. Yeung, C. P. Cheng, R. J. Herfkens, R. L. Dalman, and C. A. Taylor. Quantification of particle residence time in abdominal aortic aneurysms using magnetic resonance imaging and computational fluid dynamics. Ann. Biomed. Eng. 39:864–883, 2011.CrossRefPubMedGoogle Scholar
  58. 58.
    Suh, G. Y., A. S. Tenforde, S. C. Shadden, R. L. Spilker, C. P. Cheng, R. J. Herfkens, R. L. Dalman, and C. A. Taylor. Hemodynamic changes in abdominal aortic aneurysms with increasing exercise intensity using MR exercise imaging and image-based computational fluid dynamics. Ann. Biomed. Eng. 39:2186–2202, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Tang, B. T., S. S. Pickard, F. P. Chan, P. S. Tsao, C. A. Taylor, and J. A. Feinstein. Wall shear stress is decreased in the pulmonary arteries of patients with pulmonary arterial hypertension: an image-based, computational fluid dynamics study. Pulm. Circ. 2(4):470–476, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Taylor, C. A., M. T. Draney, J. P. Ku, D. Parker, B. N. Steele, K. Wang, and C. K. Zarins. Predictive medicine: computational techniques in therapeutic decision-making. Comput. Aided Surg. 4(5):231–247, 1999.CrossRefPubMedGoogle Scholar
  61. 61.
    Taylor, C. A., T. A. Fonte, and J. K. Min. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J. Am. Coll. Cardiol. 61(22):2233–2241, 2013.CrossRefPubMedGoogle Scholar
  62. 62.
    Taylor, C. A., T. J.R. Hughes, and C. K. Zarins. Computational investigations in vascular disease. Comput. Phys. 10(3):224–232, 1996.CrossRefGoogle Scholar
  63. 63.
    Taylor, C. A, T. J.R. Hughes, and C. K. Zarins. Finite element modeling of blood flow in arteries. Comput. Methods Appl. Mech. Eng. 158(1):155–196, 1998.CrossRefGoogle Scholar
  64. 64.
    Tran, J. S., D. E. Schiavazzi, A. B. Ramachandra, A. M. Kahn, and A. L. Marsden. Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations. Comput. Fluids, 2016.Google Scholar
  65. 65.
    Updegrove, A., N. M. Wilson, and S. C. Shadden. Boolean and smoothing of discrete polygonal surfaces. Adv. Eng. Softw. 95:16–27, 2016.CrossRefGoogle Scholar
  66. 66.
    Vignon-Clementel, I. E., C. A. Figueroa, K. E. Jansen, and C. A. Taylor. Outflow boundary conditions for three-dimensional finite element modeling of blood flow and pressure in arteries. Comput. Methods Appl. Mech. Eng. 195(29–32):3776–3796, 2006.CrossRefGoogle Scholar
  67. 67.
    Wang K. C.Y. Level set methods for computational prototyping with application to hemodynamic modeling. PhD thesis, Stanford University, 2001.Google Scholar
  68. 68.
    Wentzel, J. J., E. Janssen, J. Vos, J. C.H. Schuurbiers, R. Krams, P. W. Serruys, P. J. de Feyter, and C. J. Slager. Extension of increased atherosclerotic wall thickness into high shear stress regions is associated with loss of compensatory remodeling. Circulation 108(1):17–23, 2003.CrossRefPubMedGoogle Scholar
  69. 69.
    Whiting C. H., and K. E. Jansen. A stabilized finite element method for the incompressible Navier-Stokes equations using a hierarchical basis. Int. J. Numer. Methods Fluids 35(1):93–116, 2001.CrossRefGoogle Scholar
  70. 70.
    Wilson, N. M., F. R. Arko, and C. A. Taylor. Predicting changes in blood flow in patient-specific operative plans for treating aortoiliac occlusive disease. Comput. Aided Surg. 10(4):257–277, 2005.CrossRefPubMedGoogle Scholar
  71. 71.
    Yang, W., F. P. Chan, V. M. Reddy, A. L. Marsden, and J. A. Feinstein. Flow simulations and validation for the first cohort of patients undergoing the Y-graft Fontan procedure. J. Thorac. Cardiovascu. Surg. 149(1):247–255, 2015.CrossRefGoogle Scholar
  72. 72.
    Zhou, M., O. Sahni, H. J. Kim, C. A. Figueroa, C. A. Taylor, M. S. Shephard, and K. E. Jansen. Cardiovascular flow simulation at extreme scale. Comput. Mech., 46(1):71–82, 2010.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2016

Authors and Affiliations

  • Adam Updegrove
    • 1
  • Nathan M. Wilson
    • 5
  • Jameson Merkow
    • 2
  • Hongzhi Lan
    • 3
  • Alison L. Marsden
    • 3
    • 4
  • Shawn C. Shadden
    • 1
    • 6
  1. 1.Department of Mechanical EngineeringUniversity of CaliforniaBerkeleyUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of CaliforniaSan DiegoUSA
  3. 3.Department of BioengineeringStanford UniversityPalo AltoUSA
  4. 4.Department of PediatricsStanford UniversityPalo AltoUSA
  5. 5.Open Source Medical Software CorporationSanta MonicaUSA
  6. 6.University of CaliforniaBerkeleyUSA

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