Abstract
Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient’s anatomy while including an external device geometry remains challenging. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical μCT images. Then, by fitting the statistical model to a patient’s CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model. This model can then be used to study in silico the effects of the electrical stimulation of the cochlear implant. Results are shown on a total of 25 models of patients. In all cases, a final mesh suitable for finite element simulations was obtained, in an average time of 94 s. The framework has proven to be fast and robust, and is promising for a detailed prognosis of the cochlear implantation surgery.
Similar content being viewed by others
References
Allard, J., S. Cotin, F. Faure, P.-J. Bensoussan, F. Poyer, C. Duriez, H. Delingette, and L. Grisoni. Sofa—an open source framework for medical simulation. In: Medicine Meets Virtual Reality (MMVR’15), 2007.
Bogunovic, H., J. Pozo, R. Cardenes, M. Villa-Uriol, R. Blanc, M. Piotin, and A. Frangi. Automated landmarking and geometric characterization of the carotid siphon. Med. Image Anal. 16:889–903, 2012.
Briaire, J. J., and J. H. M. Frijns. 3D mesh generation to solve the electrical volume conduction problem in the implanted inner ear. Simul Pract. Theory. 1–2:57–73, 2000.
Bucki, M., Y. Payan, F. Cannard, B. Diot, and N. Vuillerme. Multi-modal framework for subject-specific finite element model generation aimed at pressure ulcer prevention. Comput. Methods Biomec. 16:147–148, 2013.
Ceresa, M., N. Mangado, H. Dejea, N. Carranza, P. Mistrik, H. Kjer, S. Vera, R. Paulsen, and M. González Ballester. Patient-specific simulation of implant placement and function for cochlear implantation surgery planning. In: Medical Image Computing and Computer-Assisted Intervention, LNCS, vol. 8674, 2014, pp. 49–56.
Ceresa, M., N. Mangado, R. J. Andrew, and M. González Ballester. Computational models for predicting outcomes of neuroprosthesis implantation: the case of cochlear implants. Mol. Neurobiol. 52(2):934–941, 2015
Chen, B. K., G. M. Clark, and R. Jones. Evaluation of trajectories and contact pressures for the straight nucleus cochlear implant electrode array—a two-dimensional application of finite element analysis. Med. Eng. Phys. 25:141–147, 2003.
Cootes, T. F., and C. J. Taylor. Active shape models. Their training and application. Comput. Vis Image Underst., 61(1):38–59. 1995
Duchateau, N., N. Mangado, M. Ceresa, P. Mistrik, S. Vera, and M. González Ballester. Virtual cochlear electrode insertion via parallel transport frame. In: Proceedings of International Symposium on Biomedical Imaging, 2015, pp. 1398–1401.
Escudé, B., C. James, O. Deguine, N. Cochard, E. Eter, and B. Fraysse. The size of the cochlea and predictions of insertion depth angles for cochlear implant electrodes. Audiol. Neurotol. 11:27–33, 2006.
Fang, Q., and D. Boas. Tetrahedral mesh generation from volumetric binary and gray-scale images. In: IEEE International Symposium on Biomedical Imaging, 2009.
Finley, C. C., T. A. Holden, L. K. Holden, B. R. Whiting, R. A. Chole, G. J. Neely, T. E. Hullar, and M. W. Skinner. Role of electrode placement as a contributor to variability in cochlear implant outcomes. Otol. Neurotol. 29: 920–928, 2008
Franke-Trieger, A., C. Jolly, A. Darbinjan, T. Zahnert, and D. Mürbe. Insertion depth angles of cochlear implant arrays with varying length: a temporal bone study. Otol. Neurotol. 35:58–63, 2014.
Gani, M., G. Valentini, A. Sigrist, M. I. Kós, and C. Boëx. Implications of deep electrode insertion on cochlear implant fitting. J. Assoc. Res. Otolaryngol. 29:920–928, 2008
Gomes, G. T., S. V. Cauter, M. D. Beule, L. Vigneron, C. Pattyn, and E. A. Audenaert. Biomedical Imaging and Computational Modeling in Biomechanics. Springer, Dordrecht, 2013.
Green, K. M., Y. M. Bhatt, D. J. Mawman, M. P. O’driscoll, S. Saeed, R. Ramsden, and M. Green. Predictors of audiological outcome following cochlear implantation in adults. Cochlear Implants Int. 8:1–11, 2007.
Hanekom, T. Modelling encapsulation tissue around cochlear implant electrodes. Med. Biol. Eng. Comput. 1:47–55, 2005.
Hughes, T. J. R. The Finite Element Method: Linear Static and Dynamic Finite Element Analysis. Prentice-Hall, 1987.
Kjer, H., S. Vera, J. Fagertun, M. González Ballester, and R. Paulsen. Predicting detailed inner ear anatomy from clinical pre-op CT. Int. J. Comput. Assist. Radiol. Surg. 10(Suppl1):S98–S99, 2015.
Kjer, H., J. Fagertun, S. Vera, D. Gil, M. A. González Ballester, and R. R. Paulsen. Free-form image registration of human cochlear μCT data using skeleton similarity as anatomical prior. Pattern Recognit. Lett. 1−7, 2015
Kwon, G.-H., S.-W. Chae, and K.-J. Lee. Automatic generation of tetrahedral meshes from medical images.Comput. Struct. 81:765–775, 2003.
Larrabide, I., M. Kim, L. Augsburger, M. Villa-Uriol, D. Rüfenacht, and A. Frangi. Fast virtual deployment of self-expandable stents: method and in vitro evaluation for intracranial aneurysmal stenting. Med. Image Anal. 16:721–730, 2012.
Lobos, C., and R. Rojas-Moraleda. From segmented medical images to surface and volume meshes, using existing tools and algorithms. In: International Conference on Adaptive Modeling and Simulation, 2013.
Mangado, N., M. Ceresa, N. Duchateau, H. Dejea Velardo, H. Kjer, R. Paulsen, S. Vera, P. Mistrik, J. Herrero, and M. González Ballester. Automatic generation of a computational model for monopolar stimulation of cochlear implants. Int. J. Comput. Assist. Radiol. Surg. 10(Suppl1):S67–S68, 2015.
Mangado, N., N. Duchateau, M. Ceresa, H. Kjer, S. Vera, P. Mistrik, J. Herrero, and M. González Ballester. Patient-specific virtual insertion of electrode array for electrical simulations of cochlear implants. Int. J. Comput. Assist. Radiol. Surg. 10(Suppl1):S102–S103, 2015.
Neal, M. L., and R. Kerckhoffs. Current progress in patient-specific modeling. Brief. Bioinform., 11:111–126, 2009.
Peiró, J., L. Formaggia, M. Gazzola, A. Radaelli, and V. Rigamonti. Shape reconstruction from medical images and quality mesh generation via implicit surfaces. Int. J. Numer. Methods Fluids 53:1339–1360, 2007.
Pfeiler, T. W., D. S. Lalush, and E. G. Loboa. Semiautomated finite element mesh generation methods for a long bone. Comput. Methods Prog. Biomed. 85(3):196–202. 2007
Ramos, A., and J. A. Simões. Tetrahedral versus hexahedral finite elements in numerical modelling of the proximal femur. Med. Eng. Phys. 28(9): 916–924. 2006
Rattay, F., and R. Leao. Naves and Felix, H. A model of the electrically excited human cochlear neuron. II. Influence of the three-dimensional cochlear structure on neural excitability. Hearing Res. 1–2:64–79, 2001.
Russ, C., R. Hopf, H. S. Simon, S. Born, S. Hirsch, and V. Falk. Computational stent placement in trasncatheter aortic valve implantation. In: 6th International Symposium, ISBMS, 2014.
Shepherd, J. F., and C. R. Johnson. Hexahedral mesh generation for biomedical models in SCIRun. Eng. Comput. 25:97–114, 2009.
Shewchuk, J. R. What is a good linear element? Interpolation, conditioning, and quality measures. In: 11th International Meshing Roundtable, 2002.
Sun, W., C. Martin, and T. Pham. Computational modeling of cardiac valve function and intervention. Annu. Rev. Biomed. Eng. 16:53–76, 2014.
Tabor, G., P. G. Young, T. B. West, and A. Benattayallah. Mesh construction from medical imaging for multiphysics simulation: heat transfer and fluid flow in complex geometries. Eng. Appl. Comput. Fluid. Mech. 1:126–135, 2014.
Thomas Roland, J. J. Cochlear implant electrode insertion. Oper. Tech. Otolaryngol.—Head Neck Surg. 16: 86–92, 2005.
Tran, P., A. Sue, P. Wong, Q. Li, and P. Carter. Development of HEATHER for cochlear implant stimulation using a new modeling workflow. IEEE Trans. Biomed. Eng. 62:728–735, 2015.
Zhang, J., S. Bhattacharyya, and N. Simaan. Model and parameter identification of friction during robotic insertion of cochlear-implant electrode arrays. In: IEEE Int. Conf. on Robotics and Automation, 2009.
Acknowledgments
This research was partially funded by the European Union Seventh Frame Programme (FP7/2007-2013), Grant agreement 304857, HEAR-EU project.
Author information
Authors and Affiliations
Corresponding author
Additional information
Associate Editor Xiaoxiang Zheng oversaw the review of this article.
Rights and permissions
About this article
Cite this article
Mangado, N., Ceresa, M., Duchateau, N. et al. Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation. Ann Biomed Eng 44, 2453–2463 (2016). https://doi.org/10.1007/s10439-015-1541-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-015-1541-y