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Simulation based planning of surgical interventions in pediatric cardiology
1. R. H. Haynes and A. C. Burton, “Role of the non-Newtonian behavior of blood in hemodynamics,” Am. J. Physiol. 12(197), 943–950 (1959).
2. F. J. H. Gijsen, E. Allanic, F. N. Van de Vosse, and J. D. Janssen, “The influence of the non-Newtonian properties of blood on the flow in large arteries: unsteady flow in a 90° curved tube,” J. Biomech. 32(7), 705–713 (1999).
3. I. E. Vignon-Clementel, 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, 3776–3796 (2006).
4. C. A. Figueroa, I. E. Vignon-Clementel, K. E. Jansen, T. J. Hughes, and C. A. Taylor, “A coupled momentum method for modeling blood flow in three-dimensional deformable arteries,” Comput. Methods Appl. Mech. Eng. 195(41–43), 5685–5706 (2006).
6. D. A. Steinman, “Image-based computational fluid dynamics modeling in realistic arterial geometries,” Ann. Biomed. Eng. 30(4), 483–497 (2002).
7. E. L. Bove, F. Migliavacca, M. R. de Leval, R. Balossino, G. Pennati, T. R. Lloyd, S. Khambadkone, T.-Y. Hsia, and G. Dubini, “Use of mathematic modeling to compare and predict hemodynamic effects of the modified Blalock-Taussig and right ventricle-pulmonary artery shunts for hypoplastic left heart syndrome,” J. Thorac. Cardiovasc. Surg. 136(2), 312–320 (2008).
8. K. Lagana, G. Dubini, F. Migliavacca, R. Pietrabissa, G. Pennati, A. Veneziani, and A. Quarteroni, “Multiscale modelling as a tool to prescribe realistic boundary conditions for the study of surgical procedures,” Biorheology 39, 359–364 (2002).
9. B. T. Tang, C. P. Cheng, M. T. Draney, N. M. Wilson, P. S. Tsao, R. J. Herfkens, and C. A. Taylor, “Abdominal aortic hemodynamics in young healthy adults at rest and during lower limb exercise: quantification using image-based computer modeling,” Am. J. Physiol. Heart Circ. Physiol. 291, H668–H676 (2006).
11. C. A. Taylor, C. P. Cheng, L. A. Espinosa, B. T. Tang, D. Parker, and R. J. Herfkens, “In vivo quantification of blood flow and wall shear stress in the human abdominal aorta during lower limb exercise,” Ann. Biomed. Eng. 30, 402–408 (2002).
12. C. P. Cheng, R. J. Herfkins, A. L. Lightner, C. A. Taylor, and J. A. Feinstein, “Blood flow conditions in the proximal pulmonary arteries and vena cavae: healthy children during upright cycling exercise,” Am. J. Physiol. Heart Circ. Physiol. 287(2), H921–H926 (2004).
13. Y. Bazilevs, M. C. Hsu, D. J. Benson, S. Sankaran, and A. L. Marsden, “Computational fluid-structure interaction: methods and application to a total cavopulmonary connection,” Comput. Mech. 45(1), 77–89 (2009).
14. Y. Zhang, B. Bazilevs, S. Goswami, C. L. Bajaj, and T. J. R. Hughes, “Patient-specific vascular NURBS modeling for isogeometric analysis of blood flow,” Comput. Methods Appl. Mech. Eng. 196, 2943–2959 (2007).
15. J. P. Ku, M. T. Draney, F. R. Arko, W. A. Lee, F. Chan, N. J. Pelc, C. K. Zarins, and C. A. Taylor, “In vivo validation of numerical predictions of blood flow in arterial bypass grafts,” Ann. Biomed. Eng. 30(6), 743–752 (2002).
16. A. Marsden, A. Bernstein, V. Reddy et al., “Evaluation of a novel y-shaped extracardiac Fontan baffle using computational fluid dynamics,” J. Thorac. Cardiovasc. Surg. 137(2), 394–403 (2009).
17. M. de Leval, G. Dubini et al., “Use of computational fluid dynamics in the design of surgical procedures: application to the study of competitive flows in cavopulmonary connections,” J. Thorac. Cardiovasc. Surg. 111(3), 502–513 (1996).
19. M. R. de Leval, P. Kilner, M. Gewillig, and C. Bull, “Total cavopulmonary connection: a logical alternative to atriopulmonary connection for complex Fontan operations. Experimental studies and early clinical experience,” J. Thorac. Cardiovasc. Surg. 96, 682–695 (1988).
20. J. Min, D. Berman, L. Shaw et al., “Fractional flow reserved derived from computed tomographic angiography (FFRCT) for intermediate severity coronary lesions: Results from the DeFACTO trial (determination of fractional flow reserve by anatomic computed tomographic angiography),” J. Am. Coll. Cardiol. 60(17), B6–B6 (2012).
21. S. Sankaran, M. Moghadam, A. Kahn, E. Tseng, J. Guccione, and A. Marsden, “Patient-specific multiscale modeling of blood flow for coronary artery bypass graft surgery,” Ann. Biomed. Eng. 40(10), 2228–2242 (2012).
22. D. Sengupta, A. Kahn, J. Burns, S. Sankaran, S. Shadden, and A. Marsden, “Image-based modeling of hemodynamics and coronary artery aneurysms caused by Kawasaki disease,” Biomech. Model. Mechanobiol. 11(6), 915–932 (2012).
23. A. Les, S. Shadden, C. Figueroa, J. Park, M. Tedesco, R. Herfkens, R. Dalman, and C. Taylor, “Quantification 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).
24. M. A. Castro, C. M. Putman, and J. R. Cebral, “Computational fluid dynamics modeling of intracranial aneurysms: effects of parent artery segmentation on intra-aneurysmal hemodynamics,” AJNR Am. J. Neuroradiol. 27(8), 1703–1709 (2006).
25. J. F. LaDisa Jr., L. Olson, I. Guler, D. Hettrick, S. Audi, J. Kersten, D. Warltier, and P. Pagel, “Stent design properties and deployment ratio influence indices of wall shear stress: a 3d computational fluid dynamics investigation within a normal artery,” J. Appl. Physiol. 97(1), 424–430 (2004).
26. J. F. LaDisa, C. A. Figueroa, I. E. Vignon-Clementel, H. J. Kim, N. Xiao, L. M. Ellwein, F. P. Chan, J. A. Feinstein, and C. A. Taylor, “Computational simulations for aortic coarctation: Representative results from a sampling of patients,” J. Biomech. Eng. 133(9), 091008 (2011).
27. T. Gundert, A. Marsden, W. Yang, D. Marks, and J. LaDisa, “Identification of hemodynamically optimal coronary stent designs based on vessel caliber,” IEEE Trans. Biomed. Eng. 59(7), 1992–2002 (2012).
28. T. Gundert, A. Marsden, W. Yang, and J. LaDisa, “Optimization of cardiovascular stent design using computational fluid dynamics,” ASME J. Biomech. Eng. 134(1), 011002 (2012).
30. C. Figueroa, C. Taylor, A. Chiou, V. Yeh, and C. Zarins, “Magnitude and direction of pulsatile displacement forces acting on thoracic aortic endografts,” J. Endovasc. Ther. 16(3), 350–358 (2009).
31. A. Lonyai, 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).
32. V. T. Thomas, Partners of the Heart: Vivien Thomas and His Work with Alfred Blalock: An Autobiography (University of Pennsylvania Press, 1998).
33. G. W. Miller, King of Hearts: The True Story of the Maverick Who Pioneered Open Heart Surgery (Broadway Books, 2000).
34. J. Womersley, “An elastic tube theory of pulse transmission and oscillatory flow in mammalian arteries,” Air Research and Development Command, United States Air Force, Wright Air Development Center, Wright-Patterson Air Force Base, Ohio (1957).
39. C. A. Taylor, T. J. R. Hughes, and C. K. Zarins, “Computational investigations in vascular disease,” Comput. Phys. 10(3), 224–232 (1996).
40. G. E. Karniadakis and S. J. Sherwin, Spectral/hp Element Methods for Computational Fluid Dynamics (Oxford University Press, 1999).
41. K. E. Jansen, C. H. Whiting, and G. M. Hulbert, “A generalized-α method for integrating the filtered Navier-Stokes equations with a stabilized finite element method,” Comput. Methods Appl. Mech. Eng. 190(3–4), 305–319 (2000).
42. H. J. Kim, I. E. Vignon-Clementel, C. A. Figueroa, J. F. LaDisa, K. E. Jansen, J. A. Feinstein, and C. A. Taylor, “On coupling a lumped parameter heart model and a three-dimensional finite element aorta model,” Ann. Biomed. Eng. 37(11), 2153–2169 (2009).
43. J. Muller, O. Sahni, X. Li, K. E. Jansen, M. S. Shephard, and C. A. Taylor, “Anisotropic adaptive finite element method for modeling blood flow,” Comput. Methods Biomech. Biomed. Eng. 8(5), 295–305 (2005).
44. M. E. Moghadam, Y. Bazilevs, T.-Y. Hsia, I. Vignon-Clementel, and A. Marsden, “A comparison of outlet boundary treatments for prevention of backflow divergence with relevance to blood flow simulations,” Comput. Mech. 48, 277–291 (2011).
45. G. Xiong, C. Figueroa, N. Xiao, and C. Taylor, “Simulation of blood flow in deformable vessels using subject–specific geometry and spatially varying wall properties,” Int. J. Numer. Methods Biomed. Eng. 27(7), 1000–1016 (2011).
46. M. E. Moghadam, I. Vignon-Clementel, R. Figliola, and A. Marsden, “A modular numerical method for implicit 0D/3D coupling in cardiovascular finite element simulations,” J. Comput. Phys. 244, 63–79 (2013).
47. J. P. Schmidt, S. L. Delp, M. A. Sherman, C. A. Taylor, V. S. Pande, and R. B. Altman, “The Simbios national center: Systems biology in motion,” Proc. IEEE 96(8), 1266–1280 (2008).
48. A. Saad, T. Möller, and G. Hamarneh, “Probexplorer: Uncertainty-guided exploration and editing of probabilistic medical image segmentation,” Computer Graphics Forum (Wiley Online Library, 2010), Vol. 29, pp. 1113–1122.
50. I. Simpson
, M. Woolrich
, and J. Schnabel
, “Probabilistic segmentation propagation from uncertainty in registration
,” in Proceedings Medical Image Analysis and Understanding (MIUA)
), pp. 331
; available online at http://discovery.ucl.ac.uk/1401757/
51. R. Balossino, G. Pennati, F. Migliavacca, L. Formaggia, A. Veneziani, M. Tuveri, and G. Dubini, “Computational models to predict stenosis growth in carotid arteries: Which is the role of boundary conditions?,” Comput. Methods Biomech. Biomed. Eng. 12(1), 113–123 (2009).
52. M. Zamir, The Physics of Pulsatile Flow (Springer-Verlag, New York, 2000).
53. H. Kim, I. Vignon-Clementel, C. Figueroa, K. Jansen, and C. Taylor, “Developing computational methods for three-dimensional finite element simulations of coronary blood flow,” Finite Elem. Anal. Design 46(6), 514–525 (2010).
54. H. Kim, I. Vignon-Clementel, J. Coogan, C. Figueroa, K. Jansen, and C. Taylor, “Patient-specific modeling of blood flow and pressure in human coronary arteries,” Ann. Biomed. Eng. 38(10), 3195–3209 (2010).
55. H. J. Kim, C. A. Figueroa, T. J. R. Hughes, K. E. Jansen, and C. A. Taylor, “Augmented Lagrangian method for constraining the shape of velocity profiles at outlet boundaries for three-dimensional finite element simulations of blood flow,” Comput. Methods Appl. Mech. Eng. 198(45–46), 3551–3566 (2009).
56. E. Kung, A. Les, C. Figueroa, F. Medina, K. Arcaute, R. Wicker, M. McConnell, and C. Taylor, “In vitro validation of finite element analysis of blood flow in deformable models,” Ann. Biomed. Eng. 39(7), 1947–1960 (2011).
57. E. O. Kung, F. Medina, M. V. McConnell, C. A. Taylor, R. B. Wicker, and A. S. Les, “In vitro validation of finite-element model of AAA hemodynamics incorporating realistic outlet boundary conditions,” J. Biomech. Eng. 133(4), 041003 (2011).
58. M. Vukicevic, J. A. Chiulli, T. Conover, G. Pennati, T. Y. Hsia, and R. S. Figliola, “Mock circulatory system of the Fontan circulation to study respiration effects on venous flow behavior,” Comput. Methods Appl. Mech. Eng. 59(3), 253–260 (2013).
59. B. K. Koo, A. Erglis, J. H. Doh, D. V. Daniels, S. Jegere, H. S. Kim, A. Dunning, T. Defrance, A. Lansky, J. Leipsic, and J. K. Min, “Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms,” JACC 58(19), 1989–1997 (2011).
60. A. Q. V. Agoshkov and G. Rozza, “Shape design in aorto-coronaric bypass using perturbation theory,” SIAM J. Numer. Anal. 44, 367–384 (2006).
61. A. V. Agoshkov and G. Rozza, “A mathematical approach in the design of arterial bypass anastomoses using unsteady Stokes equations,” J. Sci. Comput. 28, 139–161 (2006).
62. F. Abraham, M. Behr, and M. Heinkenschloss, “Shape optimization in unsteady blood flow: A numerical study of non-Newtonian effects,” Comput. Methods Biomech. Biomed. Eng. 8, 201–212 (2005).
64. D. D. Soerensen, K. Pekkan, D. de Zelicourt, S. Sharma, K. Kanter, M. Fogel, and A. Yoganathan, “Introduction of a new optimized total cavopulmonary connection,” Ann. Thorac. Surg. 83(6), 2182–2190 (2007).
66. A. Jameson, L. Martinelli, and N. A. Pierce, “Optimum aerodynamic design using the Navier-Stokes equations,” Theor. Comput. Fluid Dyn. 10, 213–237 (1998).
67. A. J. Booker, J. E. Dennis Jr., P. D. Frank, D. B. Serafini, V. Torczon, and M. W. Trosset, “A rigorous framework for optimization of expensive functions by surrogates,” Struct. Optim. 17(1), 1–13 (1999).
68. D. B. Serafini, “A framework for managing models in nonlinear optimization of computationally expensive functions,” Ph.D. thesis (Rice University, Houston, TX, 1998).
69. A. L. Marsden, M. Wang, J. E. Dennis Jr., and P. Moin, “Optimal aeroacoustic shape design using the surrogate management framework,” Optim. Eng. 5(2), 235–262 (2004), Special Issue: Surrogate Optimization.
70. A. L. Marsden, M. Wang, J. E. Dennis Jr., and P. Moin, “Suppression of airfoil vortex-shedding noise via derivative-free optimization,” Phys. Fluids 16(10), L83–L86 (2004).
71. A. L. Marsden, M. Wang, J. E. Dennis Jr., and P. Moin, “Trailing-edge noise reduction using derivative-free optimization and large-eddy simulation,” J. Fluid Mech. 572, 13–36 (2007).
72. C. Audet and J. E. Dennis Jr., “Mesh adaptive direct search algorithms for constrained optimization,” SIAM J. Optimization 17(1), 188–217 (2006).
75. A. L. Marsden, J. A. Feinstein, and C. A. Taylor, “A computational framework for derivative-free optimization of cardiovascular geometries,” Comput. Methods Appl. Mech. Eng. 197(21–24), 1890–1905 (2008).
76. W. Yang, J. A. Feinstein, and A. L. Marsden, “Constrained optimization of an idealized Y-shaped baffle for the Fontan surgery at rest and exercise,” Comput. Methods Appl. Mech. Eng. 199, 2135–2149 (2010).
77. W. Yang, J. A. Feinstein, S. Shadden, I. Vignon-Clementel, and A. L. Marsden, “Optimization of a y-graft design for improved hepatic flow distribution in the Fontan circulation,” J. Biomech. Eng. 135(1), 011002 (2013).
78. S. Sankaran, C. Audet, and A. Marsden, “A method for stochastic constrained optimization using derivative-free surrogate pattern search and collocation,” J. Comput. Phys. 229(12), 4664–4682 (2010).
79. S. Sankaran and A. Marsden, “The impact of uncertainty on shape optimization of idealized bypass graft models in unsteady flow,” Phys. Fluids 22(12), 121902 (2010).
80. S. Sankaran, J. D. Humphrey, and A. L. Marsden, “An efficient framework for optimization and parameter sensitivity analysis in arterial growth and remodeling computations,” Comput. Methods Appl. Mech. Eng. 256, 200–210 (2013).
81. S. Sankaran and A. Marsden, “A stochastic collocation method for uncertainty quantification and propagation in cardiovascular simulations,” J. Biomech. Eng. 133(3), 031001 (2011).
82. D. Xiu and J. Hesthaven, “High-order collocation methods for differential equations with random inputs,” SIAM J. Sci. Comput. (USA) 27(3), 1118–1139 (2005).
83. I. Babuška, F. Nobile, and R. Tempone, “A stochastic collocation method for elliptic partial differential equations with random input data,” SIAM J. Numer. Anal. 45(3), 1005–1034 (2007).
86. Y. Bazilevs, V. M. Calo, T. J. R. Hughes, and Y. Zhang, “Isogeometric fluid-structure interaction: theory, algorithms and computations,” Comput. Mech. 43, 3–37 (2008).
87. C. Long, M. Hsu, Y. Bazilevs, J. Feinstein, and A. Marsden, “Fluid–structure interaction simulations of the Fontan procedure using variable wall properties,” Int. J. Numer. Methods Biomed. Eng. 28(5), 513–527 (2012).
90. D. N. Rosenthal, A. H. Friedman, C. S. Kleinman, G. S. Kopf, L. E. Rosenfeld, and W. E. Hellenbrand, “Thromboembolic complications after Fontan operations,” Circulation 92, 287–293 (1995).
91. E. Petrossian, V. M. Reddy, K. K. Collins, C. B. Culbertson, M. J. MacDonald, J. J. Lamberti, O. Reinhartz, R. D. Mainwaring, P. D. Francis, S. P. Malhotra, D. B. Gremmels, S. Suleman, and F. L. Hanley, “The extracardiac conduit Fontan operation using minimal approach extracorporeal circulation: Early and midterm outcomes,” J. Thorac. Cardiovasc. Surg. 132(5), 1054–1063 (2006).
93. F. Migliavacca, G. Dubini, E. L. Bove, and M. R. de Leval, “Computational fluid dynamics simulations in realistic 3D geometries of the total cavopulmonary anastomosis: the influence of the inferior caval anastomosis,” J. Biomech. Eng. 125, 805–813 (2003).
94. A. L. Marsden, I. E. Vignon-Clementel, F. Chan, J. A. Feinstein, and C. A. Taylor, “Effects of exercise and respiration on hemodynamic efficiency in CFD simulations of the total cavopulmonary connection,” Ann. Biomed. Eng. 35(2), 250–263 (2007).
95. K. K. Whitehead, K. Pekkan, H. D. Kitahima, S. M. Paridon, A. P. Yoganathan, and M. A. Fogel, “Nonlinear power loss during exercise in single-ventricle patients after the Fontan: insights from computational fluid dynamics,” Circulation 116, I–165I–171 (2007).
97. A. L. Marsden, A. J. Bernstein, R. L. Spilker, F. P. Chan, C. A. Taylor, and J. A. Feinstein, “Large differences in efficiency among Fontan patients demonstrated in patient specific models of blood flow simulations,” Circulation Suppl. II 116(16), 480 (2007).
98. A. L. Marsden, V. Reddy, S. Shadden, F. Chan, C. Taylor, and J. Feinstein, “A new multiparameter approach to computational simulation for Fontan assessment and redesign,” Congenital Heart Dis. 5(2), 104–117 (2010).
99. N. A. Pike, L. A. Vricella, J. A. Feinstein, M. D. Black, and B. A. Reitz, “Regression of severe pulmonary arteriovenous malformations after Fontan revision and hepatic factor rerouting,” Ann. Thorac. Surg. 78, 697–699 (2004).
100. W. Yang, I. Vignon-Clementel, G. Troianowski, V. Reddy, J. A. Feinstein, and A. L. Marsden, “Hepatic blood flow distribution and performance in traditional and novel Y-graft Fontan geometries: A case series computational fluid dynamics study,” J. Thorac. Cardiovasc. Surg. 143(5), 1086–1097 (2012).
101. K. Kanter, C. Haggerty, M. Restrepo, D. de Zelicourt, J. Rossignac, W. Parks, and A. Yoganathan, “Preliminary clinical experience with a bifurcated y-graft Fontan procedure–A feasibility study,” J. Thorac. Cardiovasc. Surg. 144(2), 383–389 (2012).
102. G. Dubini, M. R. de Leval, R. Pietrabissa, F. M. Montevecchi, and R. Fumero, “A numerical fluid mechanical study of repaired congenital heart defects: Application to the total cavopulmonary connection,” J. Biomech. 29(1), 111–121 (1996).
103. A. E. Ensley, P. Lynch, G. P. Chatzimavroudis, C. Lucas, S. Sharma, and A. P. Yoganathan, “Toward designing the optimal total cavopulmonary connection: An in vitro study,” Ann. Thorac. Surg. 68, 1384–1390 (1999).
104. T. M. Healy, C. Lucas, and A. P. Yoganathan, “Noninvasive fluid dynamic power loss assessments for total cavopulmonary connections using the viscous dissipation function: a feasibility study,” J. Biomech. Eng. 123, 317–324 (2001).
105. K. Ryu, T. M. Healy, A. E. Ensley, S. Sharma, C. Lucas, and A. P. Yoganathan, “Importance of accurate geometry in the study of the total cavopulmonary connection: Computational simulations and in vitro experiments,” Ann. Biomed. Eng. 29, 844–853 (2001).
106. K. Pekkan, L. P. Dasi, D. de Zelicourt, K. S. Sundareswaran, M. A. Fogel, K. R. Kanter, and A. P. Yoganathan, “Hemodynamic performance of stage-2 univentricular reconstruction: Glenn vs. hemi-Fontan templates,” Ann. Biomed. Eng. 37(1), 50–63 (2008).
107. E. D. Belay, R. C. Holman, R. A. Maddox, D. A. Foster, and L. B. Schonberger, “Kawasaki syndrome hospitalizations and associated costs in the United States,” Public Health Rep. 118(5), 464–469 (2003).
108. J. C. Burns, E. V. Capparelli, J. A. Brown, J. W. Newburger, and M. P. Glode, “Intravenous gamma-globulin treatment and retreatment in Kawasaki disease,” Pediatr. Infect. Dis. J. 17(12), 1144–1148 (1998).
109. T. Akagi, Interventions in Kawasaki Disease (Springer, 2005), pp. 206–212.
110. J. F. Bastian, H. I. Kushner, E. Miller, C. Williams, C. Turner, and J. C. Burns, “Sensitivity of the Kawasaki case definition for detecting coronary artery abnormalities, Pediatr. Res. 53, 163 (2003).
111. E. S. Yellen, K. Gauvreau, A. L. Baker, M. Takahashi, J. C. Burns, C. Zambetti, J. M. Pancheri, J. R. Frazer, R. P. Sundel, D. R. Fulton, and J. W. Newburger, “New AHA recommendations improve recognition and treatment of Kawasaki disease: A multicenter retrospective review of patients with coronary aneurysms,” Circulation 116, II_660 (2007).
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Hemodynamics plays an essential role in the progression and treatment of cardiovascular disease. However, while medical imaging provides increasingly detailed anatomical information, clinicians often have limited access to hemodynamic data that may be crucial to patient risk assessment and treatment planning. Computational simulations can now provide detailed hemodynamic data to augment clinical knowledge in both adult and pediatric applications. There is a particular need for simulation tools in pediatric cardiology, due to the wide variation in anatomy and physiology in congenital heart disease patients, necessitating individualized treatment plans. Despite great strides in medical imaging, enabling extraction of flow information from magnetic resonance and ultrasound imaging, simulations offer predictive capabilities that imaging alone cannot provide. Patient specific simulations can be used for in silico testing of new surgical designs, treatment planning, device testing, and patient risk stratification. Furthermore, simulations can be performed at no direct risk to the patient. In this paper, we outline the current state of the art in methods for cardiovascular blood flow simulation and virtual surgery. We then step through pressing challenges in the field, including multiscale modeling, boundary condition selection, optimization, and uncertainty quantification. Finally, we summarize simulation results of two representative examples from pediatric cardiology: single ventricle physiology, and coronary aneurysms caused by Kawasaki disease. These examples illustrate the potential impact of computational modeling tools in the clinical setting.
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