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Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography

Although the topic of tooth fractures has been extensively analyzed in the dental literature, there is still insufficient information about the potential effect of enamel microcracks (EMCs) on the underlying tooth structures. For a precise examination of the extent of the damage to the tooth structu...

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Autores principales: Dumbryte, Irma, Vailionis, Arturas, Skliutas, Edvinas, Juodkazis, Saulius, Malinauskas, Mangirdas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292334/
https://www.ncbi.nlm.nih.gov/pubmed/34285299
http://dx.doi.org/10.1038/s41598-021-94303-4
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author Dumbryte, Irma
Vailionis, Arturas
Skliutas, Edvinas
Juodkazis, Saulius
Malinauskas, Mangirdas
author_facet Dumbryte, Irma
Vailionis, Arturas
Skliutas, Edvinas
Juodkazis, Saulius
Malinauskas, Mangirdas
author_sort Dumbryte, Irma
collection PubMed
description Although the topic of tooth fractures has been extensively analyzed in the dental literature, there is still insufficient information about the potential effect of enamel microcracks (EMCs) on the underlying tooth structures. For a precise examination of the extent of the damage to the tooth structure in the area of EMCs, it is necessary to carry out their volumetric [(three-dimensional (3D)] evaluation. The aim of this study was to validate an X-ray micro-computed tomography ([Formula: see text] CT) as a technique suitable for 3D non-destructive visualization and qualitative analysis of teeth EMCs of different severity. Extracted human maxillary premolars were examined using a [Formula: see text] CT instrument ZEISS Xradia 520 Versa. In order to separate crack, dentin, and enamel volumes a Deep Learning (DL) algorithm, part of the Dragonfly’s segmentation toolkit, was utilized. For segmentation needs we implemented Dragonfly’s pre-built UNet neural network. The scanning technique which was used made it possible to recognize and detect not only EMCs that are visible on the outer surface but also those that are buried deep inside the tooth. The 3D visualization, combined with DL assisted segmentation, enabled the evaluation of the dynamics of an EMC and precise examination of its position with respect to the dentin-enamel junction.
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spelling pubmed-82923342021-07-21 Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography Dumbryte, Irma Vailionis, Arturas Skliutas, Edvinas Juodkazis, Saulius Malinauskas, Mangirdas Sci Rep Article Although the topic of tooth fractures has been extensively analyzed in the dental literature, there is still insufficient information about the potential effect of enamel microcracks (EMCs) on the underlying tooth structures. For a precise examination of the extent of the damage to the tooth structure in the area of EMCs, it is necessary to carry out their volumetric [(three-dimensional (3D)] evaluation. The aim of this study was to validate an X-ray micro-computed tomography ([Formula: see text] CT) as a technique suitable for 3D non-destructive visualization and qualitative analysis of teeth EMCs of different severity. Extracted human maxillary premolars were examined using a [Formula: see text] CT instrument ZEISS Xradia 520 Versa. In order to separate crack, dentin, and enamel volumes a Deep Learning (DL) algorithm, part of the Dragonfly’s segmentation toolkit, was utilized. For segmentation needs we implemented Dragonfly’s pre-built UNet neural network. The scanning technique which was used made it possible to recognize and detect not only EMCs that are visible on the outer surface but also those that are buried deep inside the tooth. The 3D visualization, combined with DL assisted segmentation, enabled the evaluation of the dynamics of an EMC and precise examination of its position with respect to the dentin-enamel junction. Nature Publishing Group UK 2021-07-20 /pmc/articles/PMC8292334/ /pubmed/34285299 http://dx.doi.org/10.1038/s41598-021-94303-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Dumbryte, Irma
Vailionis, Arturas
Skliutas, Edvinas
Juodkazis, Saulius
Malinauskas, Mangirdas
Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography
title Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography
title_full Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography
title_fullStr Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography
title_full_unstemmed Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography
title_short Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography
title_sort three-dimensional non-destructive visualization of teeth enamel microcracks using x-ray micro-computed tomography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292334/
https://www.ncbi.nlm.nih.gov/pubmed/34285299
http://dx.doi.org/10.1038/s41598-021-94303-4
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