کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5032855 | 1369999 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improved Rubin-Bodner model for the prediction of soft tissue deformations
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی پزشکی
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چکیده انگلیسی
In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue. Subsequently, a finite element model (FEM) computers the stress of each node in soft facial tissue mesh data resulted from bone displacement. Next, the Generalized Regression Neural Network (GRNN) method is implemented to obtain the relationship between the facial soft tissue deformation and the stress distribution corresponding to different CMF surgical types and to improve evaluation of elastic parameters included in the RB model. Therefore, the soft facial tissue deformation can be predicted by biomechanical properties and statistical model. Leave-one-out cross-validation is used on eleven patients. As a result, the average prediction error of our model (0.7035Â mm) is lower than those resulting from other approaches. It also demonstrates that the more accurate bio-mechanical information the model has, the better prediction performance it could achieve.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Medical Engineering & Physics - Volume 38, Issue 11, November 2016, Pages 1369-1375
Journal: Medical Engineering & Physics - Volume 38, Issue 11, November 2016, Pages 1369-1375
نویسندگان
Zhang Guangming, Xia James J., Michael Liebschner, Zhang Xiaoyan, Kim Daeseung, Zhou Xiaobo,