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Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty

BACKGROUND: To explore the feasibility and effectiveness of the metal artifact reduction software (MARs) reconstruction algorithm in reducing metal artifacts of knee prostheses and to explore the optimal monochromatic level of virtual monochromatic spectral (VMS) images for artifact reduction to pro...

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Autores principales: Zhang, Jinge, Wang, Xiaozhou, Zhao, Fei, Zhang, Kai, Li, Yuming, Zhang, Yu, Zeng, Yi, Xia, Chunchao, Li, Zhenlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469117/
https://www.ncbi.nlm.nih.gov/pubmed/36111031
http://dx.doi.org/10.21037/atm-22-3286
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author Zhang, Jinge
Wang, Xiaozhou
Zhao, Fei
Zhang, Kai
Li, Yuming
Zhang, Yu
Zeng, Yi
Xia, Chunchao
Li, Zhenlin
author_facet Zhang, Jinge
Wang, Xiaozhou
Zhao, Fei
Zhang, Kai
Li, Yuming
Zhang, Yu
Zeng, Yi
Xia, Chunchao
Li, Zhenlin
author_sort Zhang, Jinge
collection PubMed
description BACKGROUND: To explore the feasibility and effectiveness of the metal artifact reduction software (MARs) reconstruction algorithm in reducing metal artifacts of knee prostheses and to explore the optimal monochromatic level of virtual monochromatic spectral (VMS) images for artifact reduction to provide high-quality images and reliable diagnosis in patients after total knee arthroplasty (TKA). METHODS: A total of 31 patients underwent gemstone spectral computed tomography. VMS images with MARs and without MARs were obtained at different energy levels (80, 100, 120, and 140 keV). Two observers scored each group of images, and interobserver agreement was evaluated. Artificial indices (AIs), percentage(500HU) and structural similarity index measure (SSIM) values were calculated in the objective analysis to evaluate the image quality and impact of metal artifacts. RESULTS: The consistency of the scores of the 2 observers was good (kappa value =0.78), and the score of the VMS images with MARs was higher than that of VMS images without MARs. AI values and percentage(500HU) of the MARs group were significantly lower than those of the without MARs group, while SSIM values were significantly higher. In the comparison of different keV images, the AI value decreased with the increase in keV in the range of 80–120 keV, but there was no significant difference between the 120 keV images and 140 keV images. In the group with MARs, the percentage(500HU) of 100–140 keV images was significantly lower than that of the 80 keV images, but there was no significant difference between 100, 120, and 140 keV images. In the group without MARs, the percentage(500HU) was significantly different among all keV groups. CONCLUSIONS: VMS images combined with the MARs algorithm can significantly reduce the metal artifacts of knee prostheses and improve image quality. At an energy level of 100–120 keV, a good metal artifact removal effect and soft tissue contrast can be achieved, and the best metal artifact removal effect can be achieved at 140 keV.
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spelling pubmed-94691172022-09-14 Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty Zhang, Jinge Wang, Xiaozhou Zhao, Fei Zhang, Kai Li, Yuming Zhang, Yu Zeng, Yi Xia, Chunchao Li, Zhenlin Ann Transl Med Original Article BACKGROUND: To explore the feasibility and effectiveness of the metal artifact reduction software (MARs) reconstruction algorithm in reducing metal artifacts of knee prostheses and to explore the optimal monochromatic level of virtual monochromatic spectral (VMS) images for artifact reduction to provide high-quality images and reliable diagnosis in patients after total knee arthroplasty (TKA). METHODS: A total of 31 patients underwent gemstone spectral computed tomography. VMS images with MARs and without MARs were obtained at different energy levels (80, 100, 120, and 140 keV). Two observers scored each group of images, and interobserver agreement was evaluated. Artificial indices (AIs), percentage(500HU) and structural similarity index measure (SSIM) values were calculated in the objective analysis to evaluate the image quality and impact of metal artifacts. RESULTS: The consistency of the scores of the 2 observers was good (kappa value =0.78), and the score of the VMS images with MARs was higher than that of VMS images without MARs. AI values and percentage(500HU) of the MARs group were significantly lower than those of the without MARs group, while SSIM values were significantly higher. In the comparison of different keV images, the AI value decreased with the increase in keV in the range of 80–120 keV, but there was no significant difference between the 120 keV images and 140 keV images. In the group with MARs, the percentage(500HU) of 100–140 keV images was significantly lower than that of the 80 keV images, but there was no significant difference between 100, 120, and 140 keV images. In the group without MARs, the percentage(500HU) was significantly different among all keV groups. CONCLUSIONS: VMS images combined with the MARs algorithm can significantly reduce the metal artifacts of knee prostheses and improve image quality. At an energy level of 100–120 keV, a good metal artifact removal effect and soft tissue contrast can be achieved, and the best metal artifact removal effect can be achieved at 140 keV. AME Publishing Company 2022-08 /pmc/articles/PMC9469117/ /pubmed/36111031 http://dx.doi.org/10.21037/atm-22-3286 Text en 2022 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Zhang, Jinge
Wang, Xiaozhou
Zhao, Fei
Zhang, Kai
Li, Yuming
Zhang, Yu
Zeng, Yi
Xia, Chunchao
Li, Zhenlin
Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty
title Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty
title_full Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty
title_fullStr Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty
title_full_unstemmed Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty
title_short Application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty
title_sort application of metal artifact reduction software in gemstone spectral computed tomography for patients after total knee arthroplasty
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469117/
https://www.ncbi.nlm.nih.gov/pubmed/36111031
http://dx.doi.org/10.21037/atm-22-3286
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