Computational Modeling and Simulation Examples in Bioengineering. Группа авторов

Чтение книги онлайн.

      91 91 Yushkevich, P.A., Piven, J., Hazlett, H.C. et al. (2006). User‐guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31: 1116–1128.

      92 92 Anton, R., Chen, C.Y., Hung, M.Y. et al. (2015). Experimental and computational investigation of the patient‐specific abdominal aortic aneurysm pressure field. Comput. Methods Biomech. Biomed. Eng. 18 (9): 981–992. https://doi.org/10.1080/10255842.2013.865024.

      93 93 Frauenfelder, T., Lotfey, M., Boehm, T., and Wildermuth, S. (2006). Computational fluid dynamics: hemodynamic changes in abdominal aortic aneurysm after stent‐graft implantation. Cardiovasc. Intervent. Radiol. 29: 613–623.

      94 94 Peattie, R.A., Riehle, T.J., and Bluth, E.I. (2004). Pulsatile flow in fusiform models of abdominal aortic aneurysms: flow fields, velocity patterns and flow‐induced wall stresses. J. Biomech. Eng. 126: 438–446.

      95 95 Dorfmann, A., Wilson, C., Edgar, E.S., and Peattie, R.A. (2010). Evaluating patient‐specific abdominal aortic aneurysm wall stress based on flow‐induced loading. Biomech. Model. Mechanobiol. 9: 127–139.

      96 96 Polzer, S., Gasser, T.C., Markert, B. et al. (2012). Impact of poroelasticity of intraluminal thrombus on wall stress of abdominal aortic aneurysms. Biomed. Eng. Online 11: 62.

      97 97 Gasser, T.C., Gorgulu, G., Folkesson, M., and Swedenborg, J. (2008). Failure properties of intraluminal thrombus in abdominal aortic aneurysm under static and pulsating mechanical loads. J. Vasc. Surg. 48: 179–188.

      98 98 Gasser, T.C., Auer, M., Labruto, F. et al. (2010). Biomechanical rupture risk assessment of abdominal aortic aneurysms: model complexity versus predictability of finite element simulations. Eur. J. Vasc. Endovasc. Surg. 40: 176–185.

      99 99 Wang, D.H.J., Makaroun, M.S., Webster, M.W., and Vorp, D.A. (2002). Effect of intraluminal thrombus on wall stress in patient specific models of abdominal aortic aneurysm. J. Vasc. Surg. I36: 598–604.

      100 100 Wang, D.H., Makaroun, M.S., Webster, M.W., and Vorp, D.A. (2001). Mechanical properties and microstructure of intraluminal thrombus from abdominal aortic aneurysm. J. Biomech. Eng. 123: 536–539.

      101 101 Ayyalasomayajula, A., Vande Geest, J.P., and Simon, B.R. (2010). Porohyperelastic finite element modeling of abdominal aortic aneurysms. J. Biomech. Eng. 132: 104502.

      102 102 Baek, S., Zambrano, B.A., Choi, J., and Lim, C.‐Y. (2014). Growth prediction of abdominal aortic aneurysms and its association of intraluminal thrombus. 11th World Congress on Computational Mechanics (WCCM XI); 5th European Conference on Computational Mechanics (ECCM V); 6th European Conference on Computational Fluid Dynamics (ECFD VI), Barcelona, Spain (20–25 July 2014).

      103 103 Zeinali‐Davarani, S. and Baek, S. (2012). Medical image‐based simulation of abdominal aortic aneurysm growth. Mech. Res. Commun. 42: 107–117.

      104 104 Vorp, D.A., Lee, P.C., Wang, D.H. et al. (2001). Association of intraluminal thrombus in abdominal aortic aneurysm with local hypoxia and wall weakening. J. Vasc. Surg. 34: 291–299.

      105 105 Biasetti, J. (2013). Physics of blood flow in arteries and its relation to intra‐luminal thrombus and atherosclerosis. Doctoral dissertation no. 84. KTH School of Engineering Sciences, Department of Solid Mechanics – vascuMECH, KTH Royal Institute of Technology, SE‐100 44 Stockholm, Sweden.

      106 106 Jones, K.C. and Mann, K.G. (1994). A model for the tissue factor pathway to thrombin.II. A mathematical simulation. J. Biol. Chem. 269: 23367–23373.

      107 107 Filipovic, N., Milasinovic, D., Zdravkovic, N. et al. (2011). Impact of aortic repair based on flow field computer simulation within the thoracic aorta. Comput. Methods Prog. Biomed. 101 (3): 243–252.

      108 108 Filipovic, N., Mijailovic, S., Tsuda, A., and Kojic, M. (2006). An implicit algorithm within the arbitrary Lagrangian–Eulerian formulation for solving incompressible fluid flow with large boundary motions. Comp. Meth. Appl. Mech. Engrg. 195: 6347–6361.

      109 109 Filipovic, N., Kojic, M., Ivanovic, M. et al. (2006). MedCFD, Specialized CFD Software for Simulation of Blood Flow Through Arteries. Serbia: University of Kragujevac.

      110 110 Perktold, K. and Rappitsch, G. (1995). Computer simulation of local blood flow and vessel mechanics in a compliant carotid artery bifurcation model. J. Biomech. 28: 845–856.

      111 111 Figueroa, C.A., Taylor, C.A., Yeh, V. et al. (2009). Effect of curvature on displacement forces acting on aortic endografts: a 3‐dimensional computational analysis. J. Endovasc. Ther. 16: 284–294.

      112 112 Figueroa, C.A., Taylor, C.A., Chiou, A.J. et al. (2009). Magnitude and direction of pulsatile displacement forces acting on thoracic aortic endografts. J. Endovasc. Ther. 16: 350–358.

      113 113 Filipovic, N., Rosic, M., Tanaskovic, I. et al. (2012). ARTreat project: Three‐dimensional numerical simulation of plaque formation and development in the arteries. IEEE Trans. Inf. Technol. Biomed. 16 (2): 272–278.

      114 114 Filipovic, N. and Schima, H. (2011). Numerical simulation of the flow field within the aortic arch during cardiac assist. Artif. Organs 35 (4): 73–83.

      115 115 Veljkovic, D., Filipovic, N., and Kojic, M. (2012). The effect of asymmetry and axial prestraining on the amplitude of mechanical stresses in abdominal aortic aneurysm. J. Mech. Med. Biol. 12 (5): 1250089.

      116 116 Krsmanovic, D., Koncar, I., Petrovic, D. et al. (2012). Computer modelling of maximal displacement forces in endoluminal thoracic aortic stent graft. Comput. Methods Biomech. Biomed. Eng. 17 (9): 1012–1020.

      117 117 Hastie, T., Tibshirani, R., and Friedman, J. (2008). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2e. New York, NY, USA: Springer.

      118 118 Kolachalama, V.B., Bressloff, N.W., and Nair, P.B. (2007). Mining data from hemodynamic simulations via Bayesian emulation. Biomed. Eng. Online 6: 47.

      119 119 Martufi, G., DiMartino, E.S., Amon, C.H. et al. (2009). Three‐dimensional geometrical characterization of abdominal aortic aneurysms: image‐based wall thickness distribution. J. Biomech. Eng. 131 (6): 061015.

      120 120 Shum, J., Martufi, G., di Martino, E. et al. (2011). Quantitative assessment of abdominal aortic aneurysm geometry. Ann. Biomed. Eng. 39 (1): 277–286.

      121 121 Filipovic, N., Ivanovic, M., Krstajic, D., and Kojic, M. (2011). Hemodynamic flow modeling through an abdominal aorta aneurysm using data mining tools. IEEE Trans. Inf. Technol. Biomed. 15 (2): 189–194.

      122 122 Pannu, H., Fadulu, V.T., Chang, J. et al. (2005). Mutations in transforming growth factor‐beta receptor type II cause familial thoracic aortic aneurysms and dissections. Circulation 112: 513–520.

      123 123 Zhu, L., Vranckx, R., Van Kien, P.K. et al. (2006). Mutations in myosin heavy chain 11 cause a syndrome associating thoracic aortic aneurysm/aortic dissection and patent ductus arteriosus. Nat. Genet. 38: 343–349.

      124 124 Renard, M., Callewaert, B., Baetens, M. et al. (2013). Novel MYH11 and ACTA2 mutations reveal a role for enhanced TGFß signaling in FTAAD. Int. J. Cardiol. 165 (2): 314–321.

      125 125 Guo, D.C., Pannu, H., Tran‐Fadulu, V. et al. (2007). Mutations in smooth muscle alpha‐actin (ACTA2) lead to thoracic aortic aneurysms and dissections. СКАЧАТЬ