TY - JOUR
T1 - A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect
AU - Kim, Daeseung
AU - Kuang, Tianshu
AU - Rodrigues, Yriu L.
AU - Gateno, Jaime
AU - Shen, Steve G.F.
AU - Wang, Xudong
AU - Stein, Kirhyn
AU - Deng, Hannah H.
AU - Liebschner, Michael A.K.
AU - Xia, James J.
N1 - Funding Information:
This work was supported in part by NIH grants ( R01 DE022676 , R01 DE027251 and R01 DE021863 ).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - Facial soft-tissue-change prediction
KW - Finite element method
KW - Lip sliding effect
KW - Orthognathic surgery
KW - Mandible
KW - Humans
KW - Orthognathic Surgery
KW - Cephalometry
KW - Retrospective Studies
KW - Maxilla
KW - Lip/surgery
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U2 - 10.1016/j.media.2021.102095
DO - 10.1016/j.media.2021.102095
M3 - Article
C2 - 34090256
AN - SCOPUS:85107271754
SN - 1361-8415
VL - 72
SP - 102095
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 102095
ER -