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 Hong Feng, Horace H. S. Ip, James J. Xia, Xianbin Cao · International Journal of Functional Informatics and Personalised Medicine · 2009
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.