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A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation

BACKGROUND: Metal artifacts appearing as streaks and shadows often compromise readability of computed tomography (CT) images. Particularly in a dental CT in which high resolution imaging is crucial for precise preparation of dental implants or orthodontic devices, reduction of metal artifacts is ver...

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Autores principales: Hegazy, Mohamed A. A., Cho, Min Hyoung, Lee, Soo Yeol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097357/
https://www.ncbi.nlm.nih.gov/pubmed/27814775
http://dx.doi.org/10.1186/s12938-016-0240-8
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author Hegazy, Mohamed A. A.
Cho, Min Hyoung
Lee, Soo Yeol
author_facet Hegazy, Mohamed A. A.
Cho, Min Hyoung
Lee, Soo Yeol
author_sort Hegazy, Mohamed A. A.
collection PubMed
description BACKGROUND: Metal artifacts appearing as streaks and shadows often compromise readability of computed tomography (CT) images. Particularly in a dental CT in which high resolution imaging is crucial for precise preparation of dental implants or orthodontic devices, reduction of metal artifacts is very important. However, metal artifact reduction algorithms developed for a general medical CT may not work well in a dental CT since teeth themselves also have high attenuation coefficients. METHODS: To reduce metal artifacts in dental CT images, we made prior images by weighted summation of two images: one, a streak-reduced image reconstructed from the metal-region-modified projection data, and the other a metal-free image reconstructed from the original projection data followed by metal region deletion. To make the streak-reduced image, we precisely segmented the metal region based on adaptive local thresholding, and then, we modified the metal region on the projection data using linear interpolation. We made forward projection of the prior image to make the prior projection data. We replaced the pixel values at the metal region in the original projection data with the ones taken from the prior projection data, and then, we finally reconstructed images from the replaced projection data. To validate the proposed method, we made computational simulations and also we made experiments on teeth phantoms using a micro-CT. We compared the results with the ones obtained by the fusion prior-based metal artifact reduction (FP-MAR) method. RESULTS: In the simulation studies using a bilateral prostheses phantom and a dental phantom, the proposed method showed a performance similar to the FP-MAR method in terms of the edge profile and the structural similarity index when an optimal global threshold was chosen for the FP-MAR method. In the imaging studies of teeth phantoms, the proposed method showed a better performance than the FP-MAR method in reducing the streak artifacts without introducing any contrast anomaly. CONCLUSIONS: The simulation and experimental imaging studies suggest that the proposed method can be used for reducing metal artifacts in dental CT images.
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spelling pubmed-50973572016-11-07 A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation Hegazy, Mohamed A. A. Cho, Min Hyoung Lee, Soo Yeol Biomed Eng Online Research BACKGROUND: Metal artifacts appearing as streaks and shadows often compromise readability of computed tomography (CT) images. Particularly in a dental CT in which high resolution imaging is crucial for precise preparation of dental implants or orthodontic devices, reduction of metal artifacts is very important. However, metal artifact reduction algorithms developed for a general medical CT may not work well in a dental CT since teeth themselves also have high attenuation coefficients. METHODS: To reduce metal artifacts in dental CT images, we made prior images by weighted summation of two images: one, a streak-reduced image reconstructed from the metal-region-modified projection data, and the other a metal-free image reconstructed from the original projection data followed by metal region deletion. To make the streak-reduced image, we precisely segmented the metal region based on adaptive local thresholding, and then, we modified the metal region on the projection data using linear interpolation. We made forward projection of the prior image to make the prior projection data. We replaced the pixel values at the metal region in the original projection data with the ones taken from the prior projection data, and then, we finally reconstructed images from the replaced projection data. To validate the proposed method, we made computational simulations and also we made experiments on teeth phantoms using a micro-CT. We compared the results with the ones obtained by the fusion prior-based metal artifact reduction (FP-MAR) method. RESULTS: In the simulation studies using a bilateral prostheses phantom and a dental phantom, the proposed method showed a performance similar to the FP-MAR method in terms of the edge profile and the structural similarity index when an optimal global threshold was chosen for the FP-MAR method. In the imaging studies of teeth phantoms, the proposed method showed a better performance than the FP-MAR method in reducing the streak artifacts without introducing any contrast anomaly. CONCLUSIONS: The simulation and experimental imaging studies suggest that the proposed method can be used for reducing metal artifacts in dental CT images. BioMed Central 2016-11-04 /pmc/articles/PMC5097357/ /pubmed/27814775 http://dx.doi.org/10.1186/s12938-016-0240-8 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hegazy, Mohamed A. A.
Cho, Min Hyoung
Lee, Soo Yeol
A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation
title A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation
title_full A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation
title_fullStr A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation
title_full_unstemmed A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation
title_short A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation
title_sort metal artifact reduction method for a dental ct based on adaptive local thresholding and prior image generation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097357/
https://www.ncbi.nlm.nih.gov/pubmed/27814775
http://dx.doi.org/10.1186/s12938-016-0240-8
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