Algorithm for Reconstructing a 3D Model of the Aortic Root Using Uniform Crushing of CT Images
The aim of the study was to develop a uniform crushing method to be used in reconstructing a computational grid for 3D models simulating transcatheter aortic valve implantation based on medical images (DICOM) and mathematical processing and then to compare this novel approach with the currently used polygon and CAD methods.
Materials and Methods. The method was developed using the clinical and imaging data of patient N., 68 years old, who underwent transcatheter aortic valve replacement. The images were taken from multispiral computed tomography. The aortic root models were reconstructed using three methods: the polygons method (Mimics, Belgium), the CAD method (NX 9.0, Germany) and the author’s algorithm implemented in MATLAB (USA). Quality assessment of the reconstructed models was performed by numerical simulation using the Abaqus/CAE 6.14 (USA) engineering analysis reproducing two pressure load scenarios: the systole and the diastole.
Results. Evaluation of the reconstructed models of the aortic root showed that the proposed numerical method allows one to segment the object (aortic root) into elements, which are more homogeneous as compared to the polygon method or the CAD method. Thus, in the polygon method, 128,452 tetrahedrons (pyramids, C3D4) were used; in the CAD method, 28,456 hexahedrons (parallelograms, C3D8) were used; and in the case of the numerical method — 24,644 identical C3D8 elements were used. The results of the numerical simulations also differed: in the polygon method, the von Mises maximum was 0.262 MPa; for the CAD algorithm, 0.412 MPa; and in the numerical method, 0.359 MPa. An important criterion of the reconstruction efficiency was the computation time factor: for the polygon method, it was 458.6 s, for the CAD algorithm — 377.2 s (21.5% less), and for the proposed numerical method — 341.8 s (34.1% less).
Conclusion. The qualitative and quantitative results of the study demonstrate the usefulness of the proposed algorithm based on hexahedral finite elements in reconstructing a biological object for the purpose of numerical analysis. Using this algorithm in the transcatheter aortic valve implantation modeling process makes it possible to reduce the time of numerical analysis and increase its accuracy, which may improve the quality of preoperative planning.
- Sardari Nia P., Heuts S., Daemen J., Luyten P., Vainer J., Hoorntje J., Cheriex E., Maessen J. Preoperative planning with three-dimensional reconstruction of patient’s anatomy, rapid prototyping
simulation for endoscopic mitral valve repair. Interact Cardiovasc Thorac Surg 2017; 24(2): 163–168, https://doi.org/10.1093/icvts/ivw308.and - Sindeev S.V., Frolov S.V. Modeling the hemodynamics of the cardiovascular system with
aneurysm. Math Models Comput Simul 2017; 9(1): 108–119, https://doi.org/10.1134/s2070048217010148.cerebral - Kilic T., Yilmaz I. Transcatheter aortic valve implantation: a revolution in the therapy of elderly and high-risk patients with severe aortic stenosis. J Geriatr Cardiol 2017; 14(3): 204–217, https://doi.org/10.11909/j.issn.1671-5411.2017.03.002.
- Bogachev-Prokofiev A.V., Sharifulin R.M., Zubarev D.D., Zhuravleva I.Y., Karaskov A.M.
Short term results of transcatheter aortic valve replacement with approach. Rossijskijtransaortal kardiologiceskij zurnal 2017; 8: 51–58, https://doi.org/10.15829/1560-4071-2017-8-51-58. - Chaturvedi A., Hobbs S.K., Ling F.S., Chaturvedi A., Knight P. MRI evaluation prior to transcatheter aortic valve implantation (TAVI): when to acquire and how to interpret. Insights Imaging 2016; 7(2): 245–254, https://doi.org/10.1007/s13244-016-0470-0.
- Bockeria L.A., Alekyan B.G.,
Pursanov M.G., Mironenko V.A., Bockeria O.L., Makarenko V.N., Zakharchenko A.V., Kosenko A.I., Bazarsadayeva T.S.,Sandodze T.S., Alekhina M.A. Transcatheter aortic valve implantation: the first experience in Russia. Grudnaai serdecno -sosudistaa hirurgia 2011; 2: 4–10. - Bokeriya L.A., Gudkova R.G. Serdechnososudistaya
khirurgiya — 2015. Boleznii vrozhdennye anomalii sistemy krovoobrashcheniya [Cardiovascular surgery — 2015. Diseases and congenital anomalies of the circulatory system]. Moscow: NTsSSKhim . A.N. Bakuleva; 2016; 208 p. - Zhuravleva I.Y., Bogachev-Prokophiev A.V., Demidov D.P., Karaskov A.M. Transcatheter implantation of mitral valve prostheses: current status of the problem. Kardiologiia 2017; 57(8): 51–59, https://doi.org/10.18087/cardio.2017.8.10018.
Neragi -Miandoab S., Michler R.E. A review of most relevant complications of transcatheter aortic valve implantation. ISRN Cardiol 2013; 2013: 956252, https://doi.org/10.1155/2013/956252.- Jung J.I., Koh Y.S., Chang K. 3D printing model before and after transcatheter aortic valve implantation for a better understanding of the anatomy of
root. Korean Circ J 2016; 46(4): 588–589, https://doi.org/10.4070/kcj.2016.46.4.588.aortic - Tzamtzis S., Viquerat J., Yap J., Mullen M.J., Burriesci G. Numerical analysis of the radial force produced by the Medtronic-CoreValve and Edwards-SAPIEN after transcatheter aortic valve implantation (TAVI). Med Eng Phys 2013; 35(1): 125–130, https://doi.org/10.1016/j.medengphy.2012.04.009.
- Wald S., Liberzon A., Avrahami I. A numerical study of the hemodynamic effect of the aortic valve on coronary flow. Biomech Model Mechanobiol 2017; 17(2): 319–338, https://doi.org/10.1007/s10237-017-0962-y.
- Bianchi M., Marom G., Ghosh R.P., Fernandez H.A., Taylor J.R. Jr., Slepian M.J., Bluestein D. Effect of balloon-expandable transcatheter aortic valve replacement positioning: a patient-specific numerical model. Artif Organs 2016; 40(12): E292–E304, https://doi.org/10.1111/aor.12806.
- Klimenov V.A., Alkhimov Yu.V., Shtein A.M., Kasyanov S.V., Babikov S.A.,
Batranin A.V., Osipov S.P. The use and development of digital radiography methods for technical non-destructive testing and inspection. Kontrol’. Diagnostika 2013; 13: 31–42. - Raut S.S., Liu P.,
E.A. An approach for patient-specific multi-domain vascular mesh generation featuring spatially varying wall thickness modeling. J Biomech 2015; 48(10): 1972–1981, https://doi.org/10.1016/j.jbiomech.2015.04.006.Finol - Pavarino E., Neves L.A., Machado J.M., de Godoy M.F., Shiyou Y., Momente J.C., Zafalon G.F., Pinto A.R., Valêncio C.R. Free tools and strategies for the generation of 3D finite element meshes: modeling of the cardiac structures. Int J Biomed Imaging 2013; 2013: 540571, https://doi.org/10.1155/2013/540571.
- Yu Z., Wang J., Gao Z., Xu M., Hoshijima M. New software developments for quality mesh generation and optimization from biomedical imaging data. Comput Methods Programs Biomed 2014; 113(1): 226–240, https://doi.org/10.1016/j.cmpb.2013.08.009.
- Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man 1979; 9: 62–66, https://doi.org/10.1109/tsmc.1979.4310076.
- Ovcharenko E.A., Klyshnikov K.U., Glushkova T.V., Burago A.U., Zhuravleva I.U. Nonlinear isotropic material model of
human aortic root. Tekhnologiizhivykh sistem 2014; 11(6): 43–47. - Krishnamurthy A., Gonzales M.J., Sturgeon G., Segars W.P., McCulloch A.D. Biomechanics simulations using cubic
meshes with extraordinary nodes for isogeometric cardiac modeling. Comput Aided Geom Des 2016; 43: 27–38, https://doi.org/10.1016/j.cagd.2016.02.016.hermite