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
by Qizhen He, Jun Feng, Horace H.S. Ip, James J. Xia, Xianbin Cao
International Journal of Functional Informatics and Personalised Medicine (IJFIPM), Vol. 2, No. 2, 2009

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.

Online publication date: Sun, 02-Aug-2009

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