Title: 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
Authors: Qizhen He, Jun Feng, Horace H.S. Ip, James J. Xia, Xianbin Cao
Addresses: Department of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China. ' School of Information and Technology, Northwest University, 710069 Xi'an, China. ' Image Computing Group, Department of Computer Science, City University of Hong Kong, Hong Kong, China. ' Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, The Methodist Hospital, Houston, Texas 77030, USA. ' Department of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
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.
Keywords: orthognathic surgery; surgery planning; operation prediction; FEM; finite element method; SDM; statistical deformable model; bone-related soft tissue prediction; soft tissue deformation.
International Journal of Functional Informatics and Personalised Medicine, 2009 Vol.2 No.2, pp.217 - 230
Published online: 02 Aug 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article