An Integration of Statistical Deformable Model and Finite Element Method for Bone-Related Soft Tissue Prediction in Orthognathic Surgery Planning

Authors:
Qizhen He;Jun Feng;Horace H. Ip;James Xia;Xianbin Cao
Affiliations:
Department of Computer Science and Technology, University of Science & Technology of China, Hefei, China and Image Computing Group Department of Computer Science, City University of Hong Kong, Hon ...;Image Computing Group Department of Computer Science, City University of Hong Kong, Hong Kong, 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 Research Institute, U.S.A;Department of Computer Science and Technology, University of Science & Technology of China, Hefei, China
Venue:
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
Year:
2008

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Abstract

In this paper, we propose a novel statistical deformable model for bone-related soft-tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Model(FEM) and Statistical Deformable Model(SDM) to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based surgery simulation for sample generation and SDM for soft tissue prediction, we are able to capture the prior knowledge of bone-related soft-tissue deformation for different surgical plans. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimization. Our experiments have shown that Br-SDM is able to give comparable soft-tissue prediction accuracy with respect to conventional FEM-based prediction while only requires 10% of its computational cost.