Numerical simulation of pelvic system could lead to a better understanding of common pathology through objective and reliable analyses of pelvic mobility according to mechanical principles. In clinical context, patient-specific simulation has the potential for a proper patient-personalised cure. For this purpose, a simulable 3D geometrical model, well suited to patient anatomy, is required. However, the geometric modelling of pelvic system from medical images (MRI) is a complex operator-dependent and time-consuming process, not adapted to patient-specific applications. This paper is addressing this challenging computational problem. The objective is to develop a technique, providing a smooth, consistent, and readily usable 3D geometrical model, seamlessly from image to simulation. In this paper, we use a generic topologically-simplified B-Spline model to represent pelvic organs. The presented paper develops a Virtual Image Correlation method to find the best correlation between the geometry and the image. The final reconstructed geometrical model is to be compatible with meshing and finite element simulation. Then, a variety of tests are performed to prove the concept, through both prototypical and pelvic models. Finally, since the pelvic system is complex, including structures hardly identifiable in MRI, some feasible solutions to introduce more complex pelvic models are also discussed.

REFERENCES

1R. C. Bump, A. Mattiasson, K. Bø, L. P. Brubaker, J. O. DeLancey, P. Klarskov, B. L. Shull, A. R. Smith, Am. J. Obstet. Gynecol. 1996, 175(1), 10.
10.1016/S0002-9378(96)70243-0
CAS PubMed Web of Science® Google Scholar
2M. Dell'oro, P. Collinet, G. Robin, C. Rubod, Gynecol. Obstet. Fertil. 2013, 41(1), 58.
10.1016/j.gyobfe.2012.12.002
PubMed Web of Science® Google Scholar
3P. Lecomte-Grosbras, M. Nassirou-Diallo, J.-F. Witz, D. Marchal, J. Dequidt, S. Cotin, M. Cosson, C. Duriez, M. Brieu, in MICCAI, Nagoya, Japan, Springer, Berlin, Heidelberg 2013, 307.
Google Scholar
4O. Mayeur, G. Lamblin, P. Lecomte-Grosbras, M. Brieu, C. Rubod, M. Cosson, in Biomedical Simulation, 2014, 8789, 220.
Google Scholar
5Y. Zhang, Y. Bazilevs, S. Goswami, C. L. Bajaj, T. J. R. Hughes, Comput. Methods Appl. Mech. Eng. 2007, 196(29), 2943.
10.1016/j.cma.2007.02.009
PubMed Web of Science® Google Scholar
6M. Ruess, Z. Yosibash, N. Trabelsi, E. Rank, PAMM 2011, 11(1), 117.
10.1002/pamm.201110050
Google Scholar
7J. A. Cottrell, T. J. R. Hughes, Y. Bazilevs, Isogeometric Analysis: Toward Integration of Cad and Fea, John Wiley & Sons, United Kingdom 2009.
10.1002/9780470749081
Google Scholar
8 Diagnostic Imaging-MRI and CT of the Female Pelvis (Eds: B. Hamm, R. Forstner), Diagnostic Imaging, Springer-Verlag, Berlin Heidelberg 2007.
10.1007/978-3-540-68212-7
Google Scholar
9L. Piegl, W. Tiller, The nurbs book, 2nd ed., Monographs in visual communication, Springer-Verlag, Berlin Heidelberg 1997.
10.1007/978-3-642-59223-2
Google Scholar
10O. Mayeur, E. Jeanditgautier, J.-F. Witz, P. Lecomte-Grosbras, M. Cosson, C. Rubod, M. Brieu, in Computational Biomechanics for Medicine: From Algorithms to Models and Applications, Springer International Publishing, Cham 2017, 135.
10.1007/978-3-319-54481-6_12
Google Scholar
11C. Lee, S. Huh, T. A. Ketter, M. Unser, Comput. Biol. Med. 1998, 28(3), 309.
10.1016/S0010-4825(98)00013-4
CAS PubMed Web of Science® Google Scholar
12(Eds: N. Paragios, Y. Chen, O. Faugeras), Handbook of Mathematical Models in Computer Vision Edited by N. Paragios, Y. Chen, O. Faugeras, Springer, Boston, MA, Boston, MA 2006.
10.1007/0-387-28831-7
Google Scholar
13W. E. Lorensen, H. E. Cline, Marching cubes: A high resolution 3d surface construction algorithm, in Proceedings of the 14th Annual conference on Computer Graphics and Interactive Techniques, ACM, SIGGRAPH '87, Anaheim, California, USA 1987, 163.
Google Scholar
14R. Shekhar, E. Fayyad, R. Yagel, J. F. Cornhill, Octree-based decimation of marching cubes surfaces, in Visualization '96. Proceedings, San Francisco, CA, USA 1996, 335.
Google Scholar
15R. Kikinis, S. D. Pieper, K. G. Vosburgh, in Intraoperative Imaging and Image-Guided Therapy, Springer New York, New York, NY 2014, 277.
10.1007/978-1-4614-7657-3_19
Google Scholar
16M. Kass, A. Witkin, D. Terzopoulos, Int. J. Comput. Vis. 1988, 1(4), 321.
10.1007/BF00133570
Web of Science® Google Scholar
17C. Xu, J. L. Prince, IEEE Trans. Image Process. 1998, 7(3), 359.
10.1109/83.661186
CAS PubMed Web of Science® Google Scholar
18S. Luo, R. Li, S. Ourselin, A new deformable model using dynamic gradient vector flow and adaptive balloon forces, in APRS Workshop on Digital Image Computing, Brisbane, Queensland, Australia 2003, 9.
Google Scholar
19T. F. Cootes, C. J. Taylor, D. H. Cooper, J. Graham, Comput. Vis. Image Underst. 1995, 61(1), 38.
10.1006/cviu.1995.1004
Web of Science® Google Scholar
20T. Heimann, H.-P. Meinzer, Med. Image Anal. 2009, 13(4), 543.
10.1016/j.media.2009.05.004
PubMed Web of Science® Google Scholar
21H. Lamecker, S. Zachow, in Computational Radiology for Orthopaedic Interventions, Springer International Publishing, Cham 2016, 1.
Google Scholar
22B. Semin, H. Auradou, M. L. M. François, Eur. Phys. J. Appl. Phys. 2011, 56(1), 10701– p1–10 .
10.1051/epjap/2011110275
Web of Science® Google Scholar
23J. Réthoré, M. L. M. François, Opt. Lasers Eng. 2014, 52, 145.
10.1016/j.optlaseng.2013.06.018
Web of Science® Google Scholar
24Z. Jiang, J.-F. Witz, P. Lecomte-Grosbras, J. Dequidt, C. Duriez, M. Cosson, S. Cotin, M. Brieu, Strain 2015, 51(3), 235.
10.1111/str.12136
Web of Science® Google Scholar
25Z. Jiang, J.-F. Witz, P. Lecomte-Grosbras, J. Dequidt, S. Cotin, C. Rubod, C. Duriez, M. Brieu, Strain 2017, 53(2), e12224.
10.1111/str.12224
Web of Science® Google Scholar
26T. W. Sederberg, S. R. Parry, SIGGRAPH Comput. Graph. 1986, 20(4), 151.
10.1145/15886.15903
Google Scholar
27M. D. Buhmann, Radial Basis Functions: Theory and Implementations, Cambridge University Press, Cambridge 2003.
10.1017/CBO9780511543241
Web of Science® Google Scholar
28M. Botsch, L. Kobbelt, M. Pauly, P. Alliez, B. Levy, Polygon Mesh Processing, CRC Press Taylor & Francis Group, Hoboken, NJ 2010.
10.1201/b10688
Google Scholar
29W. Nagel, J. Microsc. 1988, 152, 597. https://doi.org/10.1111/j.1365-2818.1988.tb01425.x
10.1111/j.1365-2818.1988.tb01425.x
Google Scholar
30J. Schöberl, Comput. Vis. Sci. 1997, 1(1), 41.
10.1007/s007910050004
Google Scholar
31L. P. Kobbelt, M. Botsch, U. Schwanecke, H.-P. Seidel, Feature sensitive surface extraction from volume data, in Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, ACM, New York, NY, USA 2001, 57.
Google Scholar
32P. B. Bornemann, F. Cirak, Comput. Methods Appl. Mech. Eng. 2013, 253, 584.
10.1016/j.cma.2012.06.023
Web of Science® Google Scholar
33S. M. Kleinendorst, J. P. M. Hoefnagels, R. C. Fleerakkers, M. P. F. H. L. van Maris, E. Cattarinuzzi, C. V. Verhoosel, M. G. D. Geers, Strain 2016, 52(4), 336.
10.1111/str.12189
Web of Science® Google Scholar
34T. W. Sederberg, J. Zheng, A. Bakenov, A. Nasri, T-splines and t-nurccs, in ACM SIGGRAPH 2003 Papers, New York, NY, USA 2003, 477.
Google Scholar
35T. W. Sederberg, D. L. Cardon, G. T. Finnigan, N. S. North, J. Zheng, T. Lyche, T-spline simplification and local refinement, in ACM SIGGRAPH 2004 Papers, New York, NY, USA 2004, 276.
Google Scholar

Citing Literature

Volume55, Issue3

June 2019

e12305

Virtual image correlation of magnetic resonance images for 3D geometric modelling of pelvic organs

Abstract

REFERENCES

Citing Literature

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Virtual image correlation of magnetic resonance images for 3D geometric modelling of pelvic organs

Abstract

REFERENCES

Citing Literature

References

Related

Information