Maxillofacial surgery simulation using a mass-spring model derived from continuum and the scaled displacement method
- 220 Downloads
- 14 Citations
Abstract
Purpose
Development of a maxillofacial surgery simulation software capable of predicting a patient’s appearance after surgery.
Methods
We have derived a new mass-spring model (MSM) equivalent to a linear finite element (FE) model for cubic elements. In addition, we propose the scaled displacement method as a new method to perform the simulation more realistically.
Results
The average error of eight soft tissue landmarks measured between 0.37 and 2.01 mm except from a landmark that had an error of 4.44 mm; values close to those obtained with the linear FE method. On the other hand, the scaled displacement method allows avoiding punctual stress concentration and bending effects making a much more realistic simulation in the region of the bone cut.
Conclusions
Good results have been achieved with our two proposed methods. In addition, the simple way in which MSM can be parallelized makes it an interesting alternative to FE method.
Keywords
Maxillofacial surgery Mass-spring model Finite element method Scaled displacement method Computer simulationPreview
Unable to display preview. Download preview PDF.
References
- 1.Xia J, Phillips C, Gateno J, Teichgraeber J, Christensen A, Gliddon M et al (2006) Cost-effectiveness analysis for computer-aided surgical simulation in complex cranio-maxillofacial surgery. J Oral Maxillofac Surg 64(12): 1780–1784. doi: 10.1016/j.joms.2005.12.072 PubMedCrossRefGoogle Scholar
- 2.Zachow S, Hege HC, Deuflhard P (2006) Computer-assisted planning in cranio-maxillofacial surgery. Comput Assist Craniofac Reconstr Model 14(1): 53–64Google Scholar
- 3.Mollemans W, Schutyser F, Nadjmi N, Maes F, Suetens P (2007) Predicting soft tissue deformations for a maxillofacial surgery planning system: From computational strategies to a complete clinical validation. Med Image Anal 11(3): 282–301. doi: 10.1016/j.media.2007.02.003 PubMedCrossRefGoogle Scholar
- 4.Krüger J, Westermann R (2003) Acceleration techniques for GPU-based volume rendering. Proc IEEE Vis ConfGoogle Scholar
- 5.Buchart C, Borro D, Amundarain A (2007) A GPU interpolating reconstruction from unorganized points. Posters Proc ACM SIGGRAPHGoogle Scholar
- 6.Gibson S, Mirtich B (1997) A survey of deformable modeling in computer graphics. Mitsubishi Electr Res Lab TR-97-19Google Scholar
- 7.Moore P, Molloy D (2007) A survey of computer-based deformable models. Int Mach Vis Image Process Conf 55–66Google Scholar
- 8.Meier U, López O, Monserrat C, Juan MC, Alcañiz M (2005) Real-time deformable models for surgery simulation: a survey. Comput Methods Programs Biomed 77: 183–197. doi: 10.1016/j.cmpb.2004.11.002 PubMedCrossRefGoogle Scholar
- 9.Cotin S, Delingette H, Ayache N (2000) A hybrid elastic model allowing real-time cutting, deformations and force-feedback for surgery training and simulation. Vis Comput 16: 437–452. doi: 10.1007/PL00007215 CrossRefGoogle Scholar
- 10.Zachow S, Hierl Th, Erdmann B (2004) On the predictability of tissue changes after osteotomy planning in maxillofacial surgery: a comparison with postoperative results. Comput Assist Radiol Surg 648–653Google Scholar
- 11.Nesme M, Marchal M, Promayon E, Chabanas M, Payan Y, Faure F (2005) Physically realistic interactive simulation for biological soft tissues. Recent Res Dev Biomech 2Google Scholar
- 12.Picinbono G, Delingette H, Ayache N (2001) Non-linear and anisotropic elastic soft tissue models for medical simulation. IEEE Int Conf Robotics AutomGoogle Scholar
- 13.Mollemans W, Schutyser F, Van Cleynenbreugel J, Suetens P (2003) Tetrahedral mass spring model for fast soft tissue deformation. Int Symp Surg Soft Tissue Model 2673: 145–154. doi: 10.1007/3-540-45015-7_14 CrossRefGoogle Scholar
- 14.Holberg C, Steinhäuser S, Rudzki I (2007) Surgically assisted rapid maxillary expansion: Midfacial and cranial stress distribution. Am J Orthod Dentofacial Orthop 132(6): 776–782. doi: 10.1016/j.ajodo.2005.12.036 PubMedCrossRefGoogle Scholar
- 15.Chabanas M, Payan Y, Marecaux C, Swider P, Boutault F (2004) Comparison of linear and non-linear soft tissue models with post-operative CT scan in maxillofacial surgery. Int Symp Med Simul 19–27Google Scholar
- 16.Maciel A, Boulic R, Thalmann D (2003) Deformable tissue parameterized by properties of real biological tissue. Int Symp Surg Simul Soft Tissue Model 74–87Google Scholar
- 17.Sundaraj K, Mendoza C, Laugier C (2002) A fast method to simulate virtual deformable objects with force feedback. Contr Autom Robot Vis 1: 413–418Google Scholar
- 18.Benzley SE, Perry E, Merkley K, Clark B, Sjaardema G (1995) A comparison of all-hexahedral and all-tetrahedral Finite Element meshes for elastic and elasto-plastic analysis. Int Meshing Roundtable 179–191Google Scholar
- 19.Delingette H, Ayache N (2004) Soft tissue modeling for surgery simulation. Elsevier, OxfordGoogle Scholar
- 20.Fung YC (1993) Biomechanics: Mechanical properties of living tissues. Springer, New YorkGoogle Scholar
- 21.Martins PALS, Natal Jorge RM, Ferreira AJM (2006) A comparative study of several material models for prediction of hyperelastic properties: Application to silicone-rubber and soft tissues. Strain 42(3): 135–147. doi: 10.1111/j.1475-1305.2006.00257.x CrossRefGoogle Scholar
- 22.Gladilin E, Zachow S, Deuflhard P, Hege HC (2003) On constitutive modeling of soft tissue for the long-term prediction of cranio-maxillofacial surgery outcome. Comp Assist Radiol Surg 1256: 343–348Google Scholar
- 23.Mollemans W, Schutyser F, Nadjmi N, Suetens P (2005) Very fast soft tissue predictions with mass tensor model for maxillofacial surgery planning systems. Comp Assist Radiol Surg 1281: 491–496Google Scholar
- 24.Lim YJ, Hu J, Chang CY, Tardella N (2006) Soft tissue deformation and cutting simulation for the multimodal surgery training. Comput Based Med Syst. 19th IEEE Int Symp, pp 635–640Google Scholar
- 25.Lloyd B, Szekely G, Harders M (2007) Identification of spring parameters for deformable object simulation. IEEE Trans Vis Comput Graph 13(5): 1081–1094. doi: 10.1109/TVCG.2007.1055 PubMedCrossRefGoogle Scholar
- 26.Bianchi G, Solenthaler B, Székely G, Harders M (2004) Simultaneous topology and stiffness identification for mass-spring models based on FEM reference deformations. Med Image Comput Comput Assist IntervGoogle Scholar
- 27.Baudet V, Beuve M, Jaillet F, Shariat B, Zara F (2007) Integrating tensile parameters in 3D mass-spring system. RR-LIRIS-2007-004Google Scholar
- 28.Van Gelder A (1998) Approximate simulation of elastic membranes by triangulated spring meshes. J Graph Tools 3(2): 21–42Google Scholar
- 29.Etzmuß O, Gross J, Straßer W (2003) Deriving a particle system from continuum mechanics for the animation of deformable objects. IEEE Trans Vis Comput Graph 9(4): 538–550. doi: 10.1109/TVCG.2003.1260747 CrossRefGoogle Scholar
- 30.Wang X, Devarajan V (2005) 1D and 2D structured mass-spring models with preload. Vis Comput 21(7): 429–448. doi: 10.1007/s00371-005-0303-5 CrossRefGoogle Scholar
- 31.Delingette H (1998) Towards realistic soft tissue modeling in medical simulation. IEEE Special Issue Surg Simul 512–523Google Scholar
- 32.Humphrey JD (2003) Continuum biomechanics of soft biological tissues. Math Phys Eng Sci 459(2029): 3–46. doi: 10.1098/rspa.2002.1060 CrossRefGoogle Scholar
- 33.Vandewalle P, Schutyser F, Van Cleynenbreugel J, Suetens P (2003) Modelling of facial soft tissue growth for maxillofacial surgery planning environments. Proc Int Symp Surg Simul Soft Tissue Model 2673: 27–37. doi: 10.1007/3-540-45015-7_3 CrossRefGoogle Scholar
- 34.Haile JM (1997) Molecular dynamics simulation: elementary methods. Wiley, New YorkGoogle Scholar
- 35.Brown J, Sorkin S, Bruyns C, Latombe JC, Montgomery K, Stephanides M (2001) Real-time simulation of deformable objects: Tools and application. Comput Animat 228–258Google Scholar
- 36.Swennen GRJ, Schutyser F, Hausamen JE (2006) Three-dimensional cephalometry. Springer, BerlinGoogle Scholar