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Digital three-dimensional image fusion processes for planning and evaluating orthodontics and orthognathic surgery. A systematic review

Abstract The three important tissue groups in orthognathic surgery (facial soft tissues, facial skeleton and dentition) can be referred to as a triad. This triad plays a decisive role in planning orthognathic surgery. Technological developments have led to the development of different three-dimensio...

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Published in:International journal of oral and maxillofacial surgery 2011-04, Vol.40 (4), p.341-352
Main Authors: Plooij, Joanneke M, Maal, Thomas J.J, Haers, Piet, Borstlap, Wilfred A, Kuijpers-Jagtman, Anne Marie, Bergé, Stefaan J
Format: Article
Language:English
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Summary:Abstract The three important tissue groups in orthognathic surgery (facial soft tissues, facial skeleton and dentition) can be referred to as a triad. This triad plays a decisive role in planning orthognathic surgery. Technological developments have led to the development of different three-dimensional (3D) technologies such as multiplanar CT and MRI scanning, 3D photography modalities and surface scanning. An objective method to predict surgical and orthodontic outcome should be established based on the integration of structural (soft tissue envelope, facial skeleton and dentition) and photographic 3D images. None of the craniofacial imaging techniques can capture the complete triad with optimal quality. This can only be achieved by ‘image fusion’ of different imaging techniques to create a 3D virtual head that can display all triad elements. A systematic search of current literature on image fusion in the craniofacial area was performed. 15 articles were found describing 3D digital image fusion models of two or more different imaging techniques for orthodontics and orthognathic surgery. From these articles it is concluded, that image fusion and especially the 3D virtual head are accurate and realistic tools for documentation, analysis, treatment planning and long term follow up. This may provide an accurate and realistic prediction model.
ISSN:0901-5027
1399-0020
DOI:10.1016/j.ijom.2010.10.013