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In the field of cranio-maxillofacial (CMF) surgery, surgical simulation is becoming a very powerful tool to plan surgery and simulate surgical results before actually performing a CMF surgical procedure. Reliable prediction of facial soft tissue changes is in particular essential for better preparation and to shorten the time taken for the operation. This paper presents a surgical simulation system to predict facial soft tissue changes caused by the movement of bone segments during CMF surgery. Two experiments were designed to test the feasibility of this simulation system. The test results demonstrate the feasibility of fast and good prediction of post-operative facial appearance, with texture. Our surgical simulation system is applicable to computer-assisted CMF surgery.


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A surgical simulation system for predicting facial soft tissue deformation

Show Author's information Xiaodong Tang1Jixiang Guo1( )Peng Li2Jiancheng Lv1
Machine Intelligence Lab, Sichuan University, Chengdu 610065, China.
West China College of Stomatology, Sichuan University, Chengdu 610065, China.

Abstract

In the field of cranio-maxillofacial (CMF) surgery, surgical simulation is becoming a very powerful tool to plan surgery and simulate surgical results before actually performing a CMF surgical procedure. Reliable prediction of facial soft tissue changes is in particular essential for better preparation and to shorten the time taken for the operation. This paper presents a surgical simulation system to predict facial soft tissue changes caused by the movement of bone segments during CMF surgery. Two experiments were designed to test the feasibility of this simulation system. The test results demonstrate the feasibility of fast and good prediction of post-operative facial appearance, with texture. Our surgical simulation system is applicable to computer-assisted CMF surgery.

Keywords: cranio-maxillofacial (CMF) surgery, finite element model (FEM), soft tissue, model-view-controller

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Publication history

Revised: 11 January 2016
Accepted: 23 February 2016
Published: 14 April 2016
Issue date: June 2016

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© The Author(s) 2016

Acknowledgements

The authors would like to thank the anonymous reviewers for their comments, which helped to improve the quality of this paper. This work described in this paper was supported by a grant from the National Natural Science Foundation of China (Nos. 61402305, 61375065, 61432014, and 61432012), a grant from the National High-tech R&D Program of China (863 Program) (No. 2013AA013803), and a grant from the Department of Science and Technology of Sichuan Province (No. 2014JY0116).

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