Daeseung Kim1, Dennis Chun-Yu Ho1, Huaming Mai1, Xiaoyan Zhang1, Steve G F Shen2, Shunyao Shen2, Peng Yuan1, Siting Liu1, Guangming Zhang3, Xiaobo Zhou3, Jaime Gateno1,4, Michael A K Liebschner5, James J Xia1,2,4. 1. Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA. 2. Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiaotong University College of Medicine, Shanghai, 200011, China. 3. Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27101, USA. 4. Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, NY, 10065, USA. 5. Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030, USA.
Abstract
PURPOSE: It is clinically important to accurately predict facial soft-tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips, and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction. METHODS: In Stage One, soft-tissue change was simulated, using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft-tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect, using a nodal force constraint method. RESULTS: The results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any subregions thereof were below 1.5 mm. The overall soft-tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods. CONCLUSION: The FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach.
PURPOSE: It is clinically important to accurately predict facial soft-tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips, and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction. METHODS: In Stage One, soft-tissue change was simulated, using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft-tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect, using a nodal force constraint method. RESULTS: The results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any subregions thereof were below 1.5 mm. The overall soft-tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods. CONCLUSION: The FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach.
Authors: Sam Sheng-Pin Hsu; Jaime Gateno; R Bryan Bell; David L Hirsch; Michael R Markiewicz; John F Teichgraeber; Xiaobo Zhou; James J Xia Journal: J Oral Maxillofac Surg Date: 2012-06-12 Impact factor: 1.895
Authors: J J Xia; J Gateno; J F Teichgraeber; P Yuan; K-C Chen; J Li; X Zhang; Z Tang; D M Alfi Journal: Int J Oral Maxillofac Surg Date: 2015-12 Impact factor: 2.789
Authors: 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 Liebschner; James J Xia Journal: Med Image Comput Comput Assist Interv Date: 2019-10-10
Authors: Daeseung Kim; Tianshu Kuang; Yriu L Rodrigues; Jaime Gateno; Steve G F Shen; Xudong Wang; Kirhyn Stein; Hannah H Deng; Michael A K Liebschner; James J Xia Journal: Med Image Anal Date: 2021-05-05 Impact factor: 13.828
Authors: Hugo Santos Cunha; Cícero André da Costa Moraes; Rodrigo de Faria Valle Dornelles; Everton Luis Santos da Rosa Journal: Oral Maxillofac Surg Date: 2020-11-08