Improved Rubin–Bodner model for the prediction of soft tissue deformations

Guangming Zhang, James J. Xia, Michael Liebschner, Xiaoyan Zhang, Daeseung Kim, Xiaobo Zhou

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

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 (0.7035 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.

Original languageEnglish (US)
Pages (from-to)1369-1375
Number of pages7
JournalMedical Engineering and Physics
Volume38
Issue number11
DOIs
StatePublished - Nov 1 2016

Keywords

  • Craniomaxillofacial surgery
  • Generalized regression neural network
  • Rubin–Bodner model
  • Soft facial tissue
  • Stress distribution

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering

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