SimVascular: An Open Source Pipeline for Cardiovascular Simulation
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.
KeywordsPatient-specific modeling Image-based CFD Hemodynamics Open-source
This work was supported by the National Science Foundation SI2 program (Award No. 1407834 and 1562450) and in part by the NIH (Contract HHSN268201100035C).
Conflicts of Interest
The authors do not have conflicts of interest relevant to this manuscript.
- 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
- 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
- 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
- 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.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
- 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
- 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
- 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
- 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
- 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.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
- 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.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.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.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
- 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
- 67.Wang K. C.Y. Level set methods for computational prototyping with application to hemodynamic modeling. PhD thesis, Stanford University, 2001.Google Scholar
- 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