| Literature DB >> 34090256 |
Daeseung Kim1, Tianshu Kuang1, Yriu L Rodrigues1, Jaime Gateno2, Steve G F Shen3, Xudong Wang3, Kirhyn Stein1, Hannah H Deng1, Michael A K Liebschner4, James J Xia5.
Abstract
Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for surgical outcome improvement. We developed a novel incremental simulation approach using finite element method (FEM) with a realistic lip sliding effect to improve the prediction accuracy in the lip region. First, a lip-detailed mesh is generated based on accurately digitized lip surface points. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect along with the mucosa sliding effect. Finally, the orthognathic surgery initiated soft-tissue change is simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. Our method was quantitatively validated using 35 retrospective clinical data sets by comparing it to the traditional FEM simulation method and the FEM simulation method with mucosa sliding effect only. The surface deviation error of our method showed significant improvement in the upper and lower lips over the other two prior methods. In addition, the evaluation results using our lip-shape analysis, which reflects clinician's qualitative evaluation, also proved significant improvement of the lip prediction accuracy of our method for the lower lip and both upper and lower lips as a whole compared to the other two methods. In conclusion, the prediction accuracy in the clinically critical region, i.e., the lips, significantly improved after applying incremental simulation with realistic lip sliding effect compared with the FEM simulation methods without the lip sliding effect.Entities:
Keywords: Facial soft-tissue-change prediction; Finite element method; Lip sliding effect; Orthognathic surgery
Mesh:
Year: 2021 PMID: 34090256 PMCID: PMC8316331 DOI: 10.1016/j.media.2021.102095
Source DB: PubMed Journal: Med Image Anal ISSN: 1361-8415 Impact factor: 13.828