Br-SDM: a fast and accurate method for bone-related soft tissue prediction in orthognathic surgery planning based on the integration of SDM and FEM

Qizhen He, Jun Feng, Horace H S Ip, James J. Xia, Xianbin Cao

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

We propose a novel Statistical Deformable Model (SDM) for bone-related soft tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Method (FEM) and SDM to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based sample generation and SDM-Based soft tissue prediction, we are able to capture the prior knowledge of bone-related soft tissue deformation. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimisation. Our experiments have shown that Br-SDM is able to give comparable soft tissue prediction accuracy with respect to conventional FEM-based prediction while reducing the computation cost from O(n2) to O(n) at the same time.

Original languageEnglish (US)
Pages (from-to)217-230
Number of pages14
JournalInternational Journal of Functional Informatics and Personalised Medicine
Volume2
Issue number2
DOIs
StatePublished - 2009

Keywords

  • FEM
  • SDM
  • finite element method
  • operation prediction
  • orthognathic surgery
  • statistical deformable model
  • surgery planning

ASJC Scopus subject areas

  • Clinical Neurology

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