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Evaluation of in vivo digital root reconstruction based on anatomical characteristics of the periodontal ligament using cone beam computed tomography
- Source :
- Scientific Reports, Vol 8, Iss 1, Pp 1-9 (2018), Scientific Reports
- Publication Year :
- 2018
- Publisher :
- Nature Publishing Group, 2018.
-
Abstract
- This study’s aim was to develop and validate an approach to automatically extract and reconstruct three-dimensional (3D) digital root models from in vivo teeth based on the anatomical characteristics of the periodontal ligament using cone beam computed tomography (CBCT) data. Prior to undergoing dental extractions for orthodontic purposes, the CBCT data of each study participant were collected and imported into Mimics software to reconstruct 3D in vivo digital root models (test models). Twenty roots of 17 teeth extracted from the study’s participants were scanned using a dental scanner to obtain 3D in vitro digital root models (reference models). The 3D morphological deviation between the reference and test models was compared; the 3D size of the bucco-lingual, mesio-distal, and root length dimensions were calculated. This approach achieved an average 3D morphological deviation of 0.21 mm, and the average size error in the bucco-lingual, mesio-distal, and root length dimensions were −0.35 mm, −0.17 mm, and 0.47 mm, respectively. This new automatic extraction approach rapidly and accurately reconstructs 3D in vivo root models with detailed morphological information, and has the potential to improve diagnostic and treatment work flow in orthodontic clinics, as well as in other areas of dentistry.
- Subjects :
- Male
Models, Anatomic
Cone beam computed tomography
Computer science
Periodontal Ligament
lcsh:Medicine
Article
03 medical and health sciences
0302 clinical medicine
Imaging, Three-Dimensional
Root length
stomatognathic system
In vivo
Periodontal fiber
Humans
Tooth Root
lcsh:Science
Orthodontics
Multidisciplinary
lcsh:R
030206 dentistry
Cone-Beam Computed Tomography
Digital root
Female
lcsh:Q
030217 neurology & neurosurgery
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 8
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....bd862edc34dce88dc6a041926b4ca046