Advertisement

A New Approach of Predicting Facial Changes Following Orthognathic Surgery Using Realistic Lip Sliding Effect

  • Daeseung Kim
  • Tianshu Kuang
  • Yriu L. Rodrigues
  • Jaime Gateno
  • Steve G. F. Shen
  • Xudong Wang
  • Han Deng
  • Peng Yuan
  • David M. Alfi
  • Michael A. K. LiebschnerEmail author
  • James J. XiaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for improving surgical outcome. However, the accuracy of current prediction methods still requires further improvement in clinically critical regions, especially the lips. We develop a novel incremental simulation approach using finite element method (FEM) with realistic lip sliding effect to improve the prediction accuracy in the area around the lips. First, lip-detailed patient-specific FE mesh is generated based on accurately digitized lip surface landmarks. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect in addition to the mucosa sliding effect. The soft-tissue change is then simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. A preliminary evaluation of prediction accuracy was conducted using retrospective clinical data. The results showed that there was a significant prediction accuracy improvement in the lip region when the realistic lip sliding effect was applied along with the mucosa sliding effect.

Notes

Acknowledgment

This work was supported in part by NIH grants (R01 DE022676, R01 DE027251 and R01 DE021863).

References

  1. 1.
    Kim, D., et al.: A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery. Med. Phys. 44(8), 4252–4261 (2017)CrossRefGoogle Scholar
  2. 2.
    Kim, H., Jürgens, P., Nolte, L.-P., Reyes, M.: Anatomically-driven soft-tissue simulation strategy for cranio-maxillofacial surgery using facial muscle template model. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 61–68. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15705-9_8CrossRefGoogle Scholar
  3. 3.
    Nadjmi, N., et al.: Quantitative validation of a computer-aided maxillofacial planning system, focusing on soft tissue deformations. Ann. Maxillofac. Surg. 4(2), 171–175 (2014)CrossRefGoogle Scholar
  4. 4.
    Pan, B., et al.: Incremental kernel ridge regression for the prediction of soft tissue deformations. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 99–106. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33415-3_13CrossRefGoogle Scholar
  5. 5.
    Zhang, X., et al.: An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation. Biomech. Model. Mechanobiol. 17(2), 387–402 (2018)CrossRefGoogle Scholar
  6. 6.
    Muñoz, J.J., Jelenić, G.: Sliding contact conditions using the master–slave approach with application on geometrically non-linear beams. Int. J. Solids Struct. 41(24), 6963–6992 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daeseung Kim
    • 1
  • Tianshu Kuang
    • 1
  • Yriu L. Rodrigues
    • 1
  • Jaime Gateno
    • 1
    • 3
  • Steve G. F. Shen
    • 2
  • Xudong Wang
    • 2
  • Han Deng
    • 1
  • Peng Yuan
    • 1
  • David M. Alfi
    • 1
    • 3
  • Michael A. K. Liebschner
    • 4
    Email author
  • James J. Xia
    • 1
    • 3
    Email author
  1. 1.Department of Oral and Maxillofacial SurgeryHouston Methodist Research InstituteHoustonUSA
  2. 2.Department of Oral and Craniomaxillofacial SurgeryShanghai Ninth People’s Hospital, Shanghai Jiaotong University College of MedicineShanghaiChina
  3. 3.Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical CollegeCornell UniversityNew YorkUSA
  4. 4.Department of NeurosurgeryBaylor College of MedicineHoustonUSA

Personalised recommendations