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Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts

BACKGROUND: This study evaluated the performances of neural networks in terms of denoizing metal artifacts in computed tomography (CT) images to improve diagnosis based on the CT images of patients. METHODS: First, head-and-neck phantoms were simulated (with and without dental implants), and CT imag...

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Autores principales: Khaleghi, Goli, Hosntalab, Mohammad, Sadeghi, Mahdi, Reiazi, Reza, Mahdavi, Seied Rabi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885504/
https://www.ncbi.nlm.nih.gov/pubmed/36726421
http://dx.doi.org/10.4103/jmss.jmss_159_21
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author Khaleghi, Goli
Hosntalab, Mohammad
Sadeghi, Mahdi
Reiazi, Reza
Mahdavi, Seied Rabi
author_facet Khaleghi, Goli
Hosntalab, Mohammad
Sadeghi, Mahdi
Reiazi, Reza
Mahdavi, Seied Rabi
author_sort Khaleghi, Goli
collection PubMed
description BACKGROUND: This study evaluated the performances of neural networks in terms of denoizing metal artifacts in computed tomography (CT) images to improve diagnosis based on the CT images of patients. METHODS: First, head-and-neck phantoms were simulated (with and without dental implants), and CT images of the phantoms were captured. Six types of neural networks were evaluated for their abilities to reduce the number of metal artifacts. In addition, 40 CT patients' images with head-and-neck cancer (with and without teeth artifacts) were captured, and mouth slides were segmented. Finally, simulated noisy and noise-free patient images were generated to provide more input numbers (for training and validating the generative adversarial neural network [GAN]). RESULTS: Results showed that the proposed GAN network was successful in denoizing artifacts caused by dental implants, whereas more than 84% improvement was achieved for images with two dental implants after metal artifact reduction (MAR) in patient images. CONCLUSION: The quality of images was affected by the positions and numbers of dental implants. The image quality metrics of all GANs were improved following MAR comparison with other networks.
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spelling pubmed-98855042023-01-31 Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts Khaleghi, Goli Hosntalab, Mohammad Sadeghi, Mahdi Reiazi, Reza Mahdavi, Seied Rabi J Med Signals Sens Original Article BACKGROUND: This study evaluated the performances of neural networks in terms of denoizing metal artifacts in computed tomography (CT) images to improve diagnosis based on the CT images of patients. METHODS: First, head-and-neck phantoms were simulated (with and without dental implants), and CT images of the phantoms were captured. Six types of neural networks were evaluated for their abilities to reduce the number of metal artifacts. In addition, 40 CT patients' images with head-and-neck cancer (with and without teeth artifacts) were captured, and mouth slides were segmented. Finally, simulated noisy and noise-free patient images were generated to provide more input numbers (for training and validating the generative adversarial neural network [GAN]). RESULTS: Results showed that the proposed GAN network was successful in denoizing artifacts caused by dental implants, whereas more than 84% improvement was achieved for images with two dental implants after metal artifact reduction (MAR) in patient images. CONCLUSION: The quality of images was affected by the positions and numbers of dental implants. The image quality metrics of all GANs were improved following MAR comparison with other networks. Wolters Kluwer - Medknow 2022-11-10 /pmc/articles/PMC9885504/ /pubmed/36726421 http://dx.doi.org/10.4103/jmss.jmss_159_21 Text en Copyright: © 2022 Journal of Medical Signals & Sensors https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Khaleghi, Goli
Hosntalab, Mohammad
Sadeghi, Mahdi
Reiazi, Reza
Mahdavi, Seied Rabi
Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts
title Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts
title_full Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts
title_fullStr Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts
title_full_unstemmed Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts
title_short Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts
title_sort neural network performance evaluation of simulated and genuine head-and-neck computed tomography images to reduce metal artifacts
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885504/
https://www.ncbi.nlm.nih.gov/pubmed/36726421
http://dx.doi.org/10.4103/jmss.jmss_159_21
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