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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: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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
Pages (from-to)217-230
JournalInternational Journal of Functional Informatics and Personalised Medicine
Volume2
Issue number2
DOIs
Publication statusPublished - 2009

Research Keywords

  • orthognathic surgery planning
  • operation prediction
  • FEM
  • finite element method
  • SDM
  • statistical deformable model
  • bone-related soft tissue prediction

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