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Research Papers

Computational Fluid Dynamic Study for aTAA Hemodynamics: An Integrated Image-Based and Radial Basis Functions Mesh Morphing Approach

[+] Author and Article Information
Katia Capellini

BioCardioLab,
Fondazione CNR-Regione
Toscana “G. Monasterio,”
Ospedale del Cuore,
Via Aurelia Sud,
Massa 54100, Italy
e-mail: katia.capellini@ftgm.it

Emanuele Vignali

BioCardioLab,
Fondazione CNR-Regione
Toscana “G. Monasterio,”
Ospedale del Cuore,
Via Aurelia Sud,
Massa 54100, Italy
e-mail: emanuele.vignali@ftgm.it

Emiliano Costa

RINA Consulting S.p.A.,
Viale Cesare Pavese, 305,
Roma 00144, Italy
e-mail: emiliano.costa@rina.org

Emanuele Gasparotti

BioCardioLab,
Fondazione CNR-Regione
Toscana “G. Monasterio,”
Ospedale del Cuore,
Via Aurelia Sud,
Massa 54100, Italy
e-mail: emanuele.gasparotti@ftgm.it

Marco Evangelos Biancolini

Department of Enterprise Engineering,
University of Rome Tor Vergata,
Via del Politecnico 1,
Roma 00133, Italy
e-mail: biancolini@ing.uniroma2.it

Luigi Landini

Department of Information Engineering,
University of Pisa,
Via Girolamo Caruso, 16,
Pisa 56122, Italy
e-mail: luigi.landini@unipi.it

Vincenzo Positano

BioCardioLab,
Fondazione CNR-Regione
Toscana “G. Monasterio,”
Ospedale del Cuore,
Via Aurelia Sud,
Massa 54100, Italy
e-mail: positano@ftgm.it

Simona Celi

BioCardioLab,
Fondazione CNR-Regione
Toscana “G. Monasterio,”
Ospedale del Cuore,
Via Aurelia Sud,
Massa 54100, Italy
e-mail: s.celi@ftgm.it

1Corresponding author.

Manuscript received February 9, 2018; final manuscript received July 9, 2018; published online August 20, 2018. Assoc. Editor: Giuseppe Vairo.

J Biomech Eng 140(11), 111007 (Aug 20, 2018) (10 pages) Paper No: BIO-18-1079; doi: 10.1115/1.4040940 History: Received February 09, 2018; Revised July 09, 2018

We present a novel framework for the fluid dynamics analysis of healthy subjects and patients affected by ascending thoracic aorta aneurysm (aTAA). Our aim is to obtain indications about the effect of a bulge on the hemodynamic environment at different enlargements. Three-dimensional (3D) surface models defined from healthy subjects and patients with aTAA, selected for surgical repair, were generated. A representative shape model for both healthy and pathological groups has been identified. A morphing technique based on radial basis functions (RBF) was applied to mold the shape relative to healthy patient into the representative shape of aTAA dataset to enable the parametric simulation of the aTAA formation. Computational fluid dynamics (CFD) simulations were performed by means of a finite volume solver using the mean boundary conditions obtained from three-dimensional (PC-MRI) acquisition. Blood flow helicity and flow descriptors were assessed for all the investigated models. The feasibility of the proposed integrated approach pertaining the coupling between an RBF morphing technique and CFD simulation for aTAA was demonstrated. Significant hemodynamic changes appear at the 60% of the bulge progression. An impingement of the flow toward the bulge was observed by analyzing the normalized flow eccentricity (NFE) index.

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References

Mokashi, S. A. , and Svensson, L. G. , 2017, “Guidelines for the Management of Thoracic Aortic Disease in 2017,” Gen. Thorac. Cardiovasc. Surg., (epub).
Johansson, G. , Markström, U. , and Swedenborg, J. , 1995, “Ruptured Thoracic Aortic Aneurysms: A Study of Incidence and Mortality Rates,” J. Vasc. Surg., 21(6), pp. 985–988. [CrossRef] [PubMed]
Elefteriades, J. A. , 2002, “Natural History of Thoracic Aortic Aneurysms: Indications for Surgery, and Surgical Versus Nonsurgical Risks,” Ann. Thorac. Surg., 74(5), pp. S1877–1880. [CrossRef] [PubMed]
Kuzmik, G. A. , Sang, A. X. , and Elefteriades, J. A. , 2012, “Natural History of Thoracic Aortic Aneurysms,” J. Vasc. Surg., 56(2), pp. 565–571. [CrossRef] [PubMed]
Friedman, M. H. , Hutchins, G. M. , Bargeron, C. B. , Deters, O. J. , and Mark, F. F. , 1981, “Correlation Between Intimal Thickness and Fluid Shear in Human Arteries,” Atherosclerosis, 39(3), pp. 425–436. [CrossRef] [PubMed]
Humphrey, J. D. , and Taylor, C. A. , 2008, “Intracranial and Abdominal Aortic Aneurysms: Similarities, Differences, and Need for a New Class of Computational Models,” Annu. Rev. Biomed. Eng., 10(1), pp. 221–246. [CrossRef] [PubMed]
Chien, S. , Li, S. , and Shyy, Y. J. , 1998, “Effects of Mechanical Forces on Signal Transduction and Gene Expression in Endothelial Cells,” Hypertension (Dallas Texas), 31(1), pp. 162–169. [CrossRef]
Davies, P. F. , 1995, “Flow-Mediated Endothelial Mechanotransduction,” Physiol. Rev., 75(3), pp. 519–560. [CrossRef] [PubMed]
Langille, B. L. , 1996, “Arterial Remodeling: Relation to Hemodynamics,” Can. J. Physiol. Pharmacol., 74(7), pp. 834–841. [CrossRef] [PubMed]
Humphrey, J. D. , 2008, “Mechanisms of Arterial Remodeling in Hypertension: Coupled Roles of Wall Shear and Intramural Stress,” Hypertension, 52(2), pp. 195–200. [CrossRef] [PubMed]
Cyron, C. J. , and Humphrey, J. D. , 2017, “Growth and Remodeling of Load-Bearing Biological Soft Tissues,” Meccanica, 52(3), pp. 645–664. [CrossRef] [PubMed]
Cyron, C. J. , Aydin, R. C. , and Humphrey, J. D. , 2016, “A Homogenized Constrained Mixture (and Mechanical Analog) Model for Growth and Remodeling of Soft Tissue,” Biomech. Model. Mechanobiol., 15(6), pp. 1389–1403. [CrossRef] [PubMed]
Braeu, F. A. , Seitz, A. , Aydin, R. C. , and Cyron, C. J. , 2017, “Homogenized Constrained Mixture Models for Anisotropic Volumetric Growth and Remodeling,” Biomech. Model. Mechanobiol., 16(3), pp. 889–906. [CrossRef] [PubMed]
Taylor, C. A. , and Steinman, D. A. , 2010, “Image-Based Modeling of Blood Flow and Vessel Wall Dynamics: Applications, Methods and Future Directions: Sixth International Bio-Fluid Mechanics Symposium and Workshop, March 28–30, 2008 Pasadena, California,” Ann. Biomed. Eng., 38(3), pp. 1188–1203. [CrossRef] [PubMed]
Antiga, L. , Ene-Iordache, B. , and Remuzzi, A. , 2003, “Computational Geometry for Patient-Specific Reconstruction and Meshing of Blood Vessels From MR and CT Angiography,” IEEE Trans. Med. Imaging, 22(5), pp. 674–684. [CrossRef] [PubMed]
Antiga, L. , Piccinelli, M. , Botti, L. , Ene-Iordache, B. , Remuzzi, A. , and Steinman, D. A. , 2008, “An Image-Based Modeling Framework for Patient-Specific Computational Hemodynamics,” Med. Biol. Eng. Comput., 46(11), pp. 1097–1112. [CrossRef] [PubMed]
Bekkers, E. J. , and Taylor, C. A. , 2008, “Multiscale Vascular Surface Model Generation From Medical Imaging Data Using Hierarchical Features,” IEEE Trans. Med. Imaging, 27(3), pp. 331–341. [CrossRef] [PubMed]
Celi, S. , Martini, N. , Pastormerlo, L. E. , Positano, V. , and Berti, S. , 2017, “Multimodality Imaging for Interventional Cardiology,” Curr. Pharm. Des., 23(22), pp. 3285–3300. [CrossRef] [PubMed]
Morbiducci, U. , Ponzini, R. , Rizzo, G. , Cadioli, M. , Esposito, A. , De Cobelli, F. , Del Maschio, A. , Montevecchi, F. M. , and Redaelli, A. , 2009, “In Vivo Quantification of Helical Blood Flow in Human Aorta by Time-Resolved Three-Dimensional Cine Phase Contrast Magnetic Resonance Imaging,” Ann. Biomed. Eng., 37(3), pp. 516–531. [CrossRef] [PubMed]
Stankovic, Z. , Allen, B. D. , Garcia, J. , Jarvis, K. B. , and Markl, M. , 2014, “4D Flow Imaging With MRI,” Cardiovasc. Diagn. Ther., 4(2), pp. 173–192. [PubMed]
Bozzi, S. , Morbiducci, U. , Gallo, D. , Ponzini, R. , Rizzo, G. , Bignardi, C. , and Passoni, G. , 2017, “Uncertainty Propagation of Phase Contrast-MRI Derived Inlet Boundary Conditions in Computational Hemodynamics Models of Thoracic Aorta,” Comput. Methods Biomech. Biomed. Eng., 20(10), pp. 1104–1112. [CrossRef]
Geisbüsch, S. , Stefanovic, A. , Schray, D. , Oyfe, I. , Lin, H.-M. , Di Luozzo, G. , and Griepp, R. B. , 2014, “A Prospective Study of Growth and Rupture Risk of Small-to-Moderate Size Ascending Aortic Aneurysms,” J. Thorac. Cardiovasc. Surg., 147(1), pp. 68–74. [CrossRef] [PubMed]
Oladokun, D. , Patterson, B. O. , Sobocinski, J. , Karthikesalingam, A. , Loftus, I. , Thompson, M. M. , and Holt, P. J. , 2016, “Systematic Review of the Growth Rates and Influencing Factors in Thoracic Aortic Aneurysms,” Eur. J. Vasc. Endovasc. Surg., 51(5), pp. 674–681. [CrossRef] [PubMed]
Numata, S. , Itatani, K. , Kanda, K. , Doi, K. , Yamazaki, S. , Morimoto, K. , Manabe, K. , Ikemoto, K. , and Yaku, H. , 2016, “Blood Flow Analysis of the Aortic Arch Using Computational Fluid Dynamics,” Eur. J. Cardio-Thorac. Surg., 49(6), pp. 1578–1585. [CrossRef]
Benim, A. C. , Nahavandi, A. , Assmann, A. , Schubert, D. , Feindt, P. , and Suh, S. H. , 2011, “Simulation of Blood Flow in Human Aorta With Emphasis on Outlet Boundary Conditions,” Appl. Math. Modell., 35(7), pp. 3175–3188. [CrossRef]
Kimura, N. , Nakamura, M. , Komiya, K. , Nishi, S. , Yamaguchi, A. , Tanaka, O. , Misawa, Y. , Adachi, H. , and Kawahito, K. , 2017, “Patient-Specific Assessment of Hemodynamics by Computational Fluid Dynamics in Patients With Bicuspid Aortopathy,” J. Thorac. Cardiovasc. Surg., 153(4), pp. S52–S62. [CrossRef] [PubMed]
Youssefi, P. , Gomez, A. , He, T. , Anderson, L. , Bunce, N. , Sharma, R. , Figueroa, C. A. , and Jahangiri, M. , 2017, “Patient-Specific Computational Fluid Dynamics-Assessment of Aortic Hemodynamics in a Spectrum of Aortic Valve Pathologies,” J. Thorac. Cardiovasc. Surg., 153(1), pp. 8–20. [CrossRef] [PubMed]
Duan, Y. , 2008, “A Note on the Meshless Method Using Radial Basis Functions,” Comput. Math. Appl., 55(1), pp. 66–75. [CrossRef]
Biancolini, M. , 2012, “Mesh Morphing and Smoothing by Means of Radial Basis Functions (RBF): A practical example using Fluent and RBF-Morph,” Handbook of Research on Computational Science and Engineering: Theory and Practice, J. Leng, W. Sharrock, eds., IGI Global, Hershey, PA, pp. 347–380.
Cella, U. , Groth, C. , and Biancolini, M. E. , 2017, “Geometric Parameterization Strategies for Shape Optimization Using RBF Mesh Morphing,” Advances on Mechanics, Design Engineering and Manufacturing, Springer, Cham, Switzerland, pp. 537–545.
Biancolini, M. E. , 2018, Fast Radial Basis Functions for Engineering Applications, Springer International Publishing, Basel, Switzerland.
Turini, G. , Condino, S. , Sinceri, S. , Tamadon, I. , Celi, S. , Quaglia, C. , Murzi, M. , Soldani, G. , Menciassi, A. , Ferrari, V. , and Ferrari, M. , 2017, “Patient Specific Virtual and Physical Simulation Platform for Surgical Robot Movability Evaluation in Single-Access Robot-Assisted Minimally-Invasive Cardiothoracic Surgery,” Augmented Reality, Virtual Reality, and Computer Graphics: Fourth International Conference, AVR 2017, Part II, L. T. De Paolis , P. , Bourdot , and A. , Mongelli , eds., Springer International Publishing, Cham, Switzerland, pp. 211–220.
Antiga, L. , and Steinman, D. A. , 2004, “Robust and Objective Decomposition and Mapping of Bifurcating Vessels,” IEEE Trans. Med. Imaging, 23(6), pp. 704–713. [CrossRef] [PubMed]
Brown, A. G. , Shi, Y. , Marzo, A. , Staicu, C. , Valverde, I. , Beerbaum, P. , Lawford, P. V. , and Hose, D. R. , 2012, “Accuracy Vs. Computational Time: Translating Aortic Simulations to the Clinic,” J. Biomech., 45(3), pp. 516–523. [CrossRef] [PubMed]
Malek, A. M. , Alper, S. L. , and Izumo, S. , 1999, “Hemodynamic Shear Stress and Its Role in Atherosclerosis,” JAMA, 282(21), pp. 2035–2042. [CrossRef] [PubMed]
Ku, D. N. , Giddens, D. P. , Zarins, C. K. , and Glagov, S. , 1985, “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), pp. 293–302. [CrossRef]
Gallo, D. , Isu, G. , Massai, D. , Pennella, F. , Deriu, M. A. , Ponzini, R. , Bignardi, C. , Audenino, A. , Rizzo, G. , and Morbiducci, U. , 2014, “A Survey of Quantitative Descriptors of Arterial Flows,” Visualization and Simulation of Complex Flows in Biomedical Engineering, Springer, Dordrecht, The Netherlands, pp. 1–24.
Shtilman, L. , Levich, E. , Orszag, S. A. , Pelz, R. B. , and Tsinober, A. , 1985, “On the Role of Helicity in Complex Fluid Flows,” Phys. Lett. A, 113(1), pp. 32–37. [CrossRef]
Sigovan, M. , Hope, M. D. , Dyverfeldt, P. , and Saloner, D. , 2011, “Comparison of Four-Dimensional Flow Parameters for Quantification of Flow Eccentricity in the Ascending Aorta,” J. Magn. Reson. Imaging JMRI, 34(5), pp. 1226–1230. [CrossRef]
Ahmad, T. , Plee, S. L. , and Myers, J. P. , 2013, ANSYS Fluent Theory Guide, Ansys Inc., Canonsburg, PA.
Elefteriades, J. A. , and Farkas, E. A. , 2010, “Thoracic Aortic Aneurysm: Clinically Pertinent Controversies and Uncertainties,” J. Am. Coll. Cardiol., 55(9), pp. 841–857. [CrossRef] [PubMed]
Biancolini, M. E. , Ponzini, R. , Antiga, L. , and Morbiducci, U. , 2012, “A New Workflow for Patient Specific Image-Based Hemodynamics: Parametric Study of the Carotid Bifurcation,” Computational Modelling of Objects Represented in Images III: Fundamentals, Methods and Applications, CRC Press, Rome, Italy.
Doi, K. , 2007, “Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential,” Comput. Med. Imaging Graphics, 31(4–5), pp. 198–211. [CrossRef]
van Ginneken, B. , Schaefer-Prokop, C. M. , and Prokop, M. , 2011, “Computer-Aided Diagnosis: How to Move From the Laboratory to the Clinic,” Radiology, 261(3), pp. 719–732. [CrossRef] [PubMed]
Suzuki, K. , 2012, “A Review of Computer-Aided Diagnosis in Thoracic and Colonic Imaging,” Quant. Imaging Med. Surg., 2(3), pp. 163–176. [PubMed]
Celi, S. , and Berti, S. , 2014, “Three-Dimensional Sensitivity Assessment of Thoracic Aortic Aneurysm Wall Stress: A Probabilistic Finite-Element Study,” Eur. J. Cardio-Thorac. Surg., 45(3), pp. 467–475. [CrossRef]
Celi, S. , and Berti, S. , 2012, “Biomechanics and FE Modelling of Aneurysm: Review and Advances in Computational Models,” Aneurysm, Y. Murai , ed., IntechOpen, London, pp. 3–26.
Liljeqvist, M. L. , Hultgren, R. , Gasser, T. C. , and Roy, J. , 2016, “Volume Growth of Abdominal Aortic Aneurysms Correlates With Baseline Volume and Increasing Finite Element Analysis-Derived Rupture Risk,” J. Vasc. Surg., 63(6), pp. 1434–1442. [CrossRef] [PubMed]
Liu, X. , Pu, F. , Fan, Y. , Deng, X. , Li, D. , and Li, S. , 2009, “A Numerical Study on the Flow of Blood and the Transport of LDL in the Human Aorta: The Physiological Significance of the Helical Flow in the Aortic Arch,” Am. J. Physiol.: Heart Circ. Physiol., 297(1), pp. H163–170. [CrossRef] [PubMed]
Morbiducci, U. , Ponzini, R. , Rizzo, G. , Cadioli, M. , Esposito, A. , Montevecchi, F. M. , and Redaelli, A. , 2011, “Mechanistic Insight Into the Physiological Relevance of Helical Blood Flow in the Human Aorta: An In Vivo Study,” Biomech. Model. Mechanobiol., 10(3), pp. 339–355. [CrossRef] [PubMed]
Weigang, E. , Kari, F. A. , Beyersdorf, F. , Luehr, M. , Etz, C. D. , Frydrychowicz, A. , Harloff, A. , and Markl, M. , 2008, “Flow-Sensitive Four-Dimensional Magnetic Resonance Imaging: Flow Patterns in Ascending Aortic Aneurysms,” Eur. J. Cardiothorac. Surg., 34(1), pp. 11–16. [CrossRef] [PubMed]
Frydrychowicz, A. , Arnold, R. , Hirtler, D. , Schlensak, C. , Stalder, A. F. , Hennig, J. , Langer, M. , and Markl, M. , 2008, “Multidirectional Flow Analysis by Cardiovascular Magnetic Resonance in Aneurysm Development Following Repair of Aortic Coarctation,” J. Cardiovasc. Magn. Reson., 10(1), p. 30. [CrossRef] [PubMed]
Kilner, P. J. , Yang, G. Z. , Mohiaddin, R. H. , Firmin, D. N. , and Longmore, D. B. , 1993, “Helical and Retrograde Secondary Flow Patterns in the Aortic Arch Studied by Three-Directional Magnetic Resonance Velocity Mapping,” Circulation, 88(5), pp. 2235–2247. [CrossRef] [PubMed]
Pasta, S. , Rinaudo, A. , Luca, A. , Pilato, M. , Scardulla, C. , Gleason, T. G. , and Vorp, D. A. , 2013, “Difference in Hemodynamic and Wall Stress of Ascending Thoracic Aortic Aneurysms With Bicuspid and Tricuspid Aortic Valve,” J. Biomech., 46(10), pp. 1729–1738. [CrossRef] [PubMed]
Boccadifuoco, A. , Mariotti, A. , Celi, S. , Martini, N. , and Salvetti, M. V. , 2016, “Uncertainty Quantification in Numerical Simulations of the Flow in Thoracic Aortic Aneurysms,” Institute of Structural Analysis and Antiseismic Research, School of Civil Engineering, National Technical University of Athens, NTUA, Athens, Greece, pp. 6226–6249.
Boccadifuoco, A. , Mariotti, A. , Celi, S. , Martini, N. , and Salvetti, M. V. , 2018, “Impact of Uncertainties in Outflow Boundary Conditions on the Predictions of Hemodynamic Simulations of Ascending Thoracic Aortic Aneurysms,” Comput. Fluids, 165, pp. 96–115. [CrossRef]
Bianchi, D. , Monaldo, E. , Gizzi, A. , Marino, M. , Filippi, S. , and Vairo, G. , 2017, “A FSI Computational Framework for Vascular Physiopathology: A Novel Flow-Tissue Multiscale Strategy,” Med. Eng. Phys., 47, pp. 25–37. [CrossRef] [PubMed]
Formaggia, L. , Quarteroni, A. , and Veneziani, A. , 2009, Cardiovascular Mathematics: Modeling and Simulation of the Circulatory System, Springer-Verlag, Mailand, Italy.

Figures

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Fig. 1

Definition of the RBF mesh morphing process applied to the ascending aorta. Source and target points correspond to the healthy and pathological aorta respectively. (Source in red and target in blue in the colored online version).

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Fig. 2

Results of the RBF mesh morphing phase: SM20 (a), SM40 (b), SM60 (c), SM80 (d), and SM100 (e)

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Fig. 3

Results of the effect of the morphing both at geometry and mesh quality level. Histogram of the maximum distance between the SSA and SM100 geometries (a). Map of the errgeom between the SSA and the SM100 geometries (b). Number of cells with a skewness value greater than 0.85 at different percentage of the morphed bulge (c) and localization of the cells with a skewness value greater than 0.85 in SSC and SM100 (d). The value equal to 0.85 has been chosen as an intermediate value between the imposed target skewness of 0.75 and the maximum suggested (0.95) by the CFD code.

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Fig. 4

Scheme of the 0D–3D coupling assumption with the three parameters defined for the Windkessel models and results of the flow and pressure curves for the four outlets and the inlet

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Fig. 5

Streamlines at systole for four configurations: the SSC (a), the SM60 (b), the SM80 (c), and SM100 (d)

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Fig. 6

Vector plots of the in-plane velocity (a)–(d) and contour plots of the through-plane velocity (e)–(h) for SSC (a) and (e), SM60 (b) and (f), SM80 (c) and (g) and SM100 (d) and (h). Plots of the NFE index for SSC (i), SM60 (j), SM80 (k), and SM100 (l). All plots correspond to T2 time.

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Fig. 7

TAWSS (a)–(d) and OSI (e)–(h) resulting from the CFD analysis for the SSC, SM60, SM80, SM100 geometries

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Fig. 8

Isosurfaces of LNH at three different phases of the cardiac cycle (T1, T2, and T3) for each one of the four configurations: SSC (a)–(c), SM60 (d)–(f), SM80 (g)–(i), and SM100 (j)–(l). High threshold values of LNH (= ±0.8) are used for the visualization of markedly aligned/opposed velocity and vorticity vector fields. Positive and negative LNH values indicate counter-rotating flow structures.

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