Voxel-based superimposition of Cone Beam CT scans for orthodontic and craniofacial follow-up: Overview and clinical implementation
| Autor: | Benjamin Salmon, Gauthier Dot, Frédéric Rafflenbeul |
|---|---|
| Přispěvatelé: | Pathologies, Imagerie et Biothérapies oro-faciales (URP 2496), Université de Paris (UP) |
| Rok vydání: | 2020 |
| Předmět: |
Cone beam computed tomography
Adolescent Computer science [SDV]Life Sciences [q-bio] Human error Image processing Orthodontics computer.software_genre 03 medical and health sciences Young Adult 0302 clinical medicine Software Imaging Three-Dimensional Voxel Image Processing Computer-Assisted Superimposition Humans Computer vision 030212 general & internal medicine Craniofacial Child Skull Base business.industry Orthognathic Surgery Skull Process (computing) 030206 dentistry Cone-Beam Computed Tomography Artificial intelligence Anatomic Landmarks business computer Algorithms |
| Zdroj: | International Orthodontics International Orthodontics, Elsevier Masson, 2020, 18, pp.739-748. ⟨10.1016/j.ortho.2020.08.001⟩ |
| ISSN: | 1879-680X 1761-7227 |
| Popis: | Summary Introduction The increasing use of three-dimensional (3D) imaging in orthodontics has led to the development of 3D superimposition techniques. These techniques use stable anatomic structures as references in order to compare Cone Beam CT (CBCT) scans of the same subject at different time-points. Three methods have been described in the literature: landmark-based, surface-based and voxel-based 3D superimpositions. Objective This article focuses on the voxel-based approach, which is the most described and the only one that can be fully automatized. The aim of this paper is to offer clinicians a practical tutorial on craniofacial voxel-based 3D superimposition. Material and Methods We provide an updated overview of the available implementation methods, describing their methodology, validations, main steps, advantages and drawbacks. The historical open-source method is the most widespread for research purposes, but takes around three hours to achieve for an experienced operator. Several commercially-available software perform superimpositions in a few minutes. Results We used two of the available methods to conduct the superimposition process with three representative clinical cases in order to illustrate the different types of results that can be obtained. Conclusions Commercially-available software provide user-friendly and fully automatized superimposition methods, allowing clinicians to perform it easily and helping to reduce human error in image analysis. Still, quantitative evaluation of the results remains the main challenge of this technique. |
| Databáze: | OpenAIRE |
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