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Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software
OBJECTIVE: To evaluate the practical effectiveness of smart metal artifact reduction (SMAR) in reducing artifacts caused by metallic implants. METHODS: Patients with metal implants underwent computed tomography (CT) examinations on high definition CT scanner, and the data were reconstructed with ada...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7874696/ https://www.ncbi.nlm.nih.gov/pubmed/33817081 http://dx.doi.org/10.1515/biol-2018-0021 |
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author | Zhou, Peng Zhang, Chunling Gao, Zhen Cai, Wangshu Yan, Deyue Wei, Zhaolong |
author_facet | Zhou, Peng Zhang, Chunling Gao, Zhen Cai, Wangshu Yan, Deyue Wei, Zhaolong |
author_sort | Zhou, Peng |
collection | PubMed |
description | OBJECTIVE: To evaluate the practical effectiveness of smart metal artifact reduction (SMAR) in reducing artifacts caused by metallic implants. METHODS: Patients with metal implants underwent computed tomography (CT) examinations on high definition CT scanner, and the data were reconstructed with adaptive statistical iterative reconstruction (ASiR) with value weighted to 40% and smart metal artifact reduction (SMAR) technology. The comparison was assessed by both subjective and objective assessment between the two groups of images. In terms of subjective assessment, three radiologists evaluated image quality and assigned a score for visualization of anatomic structures in the critical areas of interest. Objectively, the absolute CT value of the difference (ΔCT) and artifacts index (AI) were adopted in this study for the quantitative assessment of metal artifacts. RESULTS: In subjective image quality assessment, three radiologists scored SMAR images higher than 40% ASiR images (P<0.01) and the result suggested that visualization of critical anatomic structures around the region of the metal object was significantly improved by using SMAR compared with 40% ASiR. The ΔCT and AI for quantitative assessment of metal artifacts showed that SMAR appeared to be superior for reducing metal artifacts (P<0.05) and indicated that this technical approach was more effective in improving the quality of CT images. CONCLUSION: A variety of hardware (dental filling, embolization coil, instrumented spine, hip implant, knee implant) are processed with the SMAR algorithm to demonstrate good recovery of soft tissue around the metal. This artifact reduction allows for the clearer visualization of structures hidden underneath. |
format | Online Article Text |
id | pubmed-7874696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
spelling | pubmed-78746962021-04-01 Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software Zhou, Peng Zhang, Chunling Gao, Zhen Cai, Wangshu Yan, Deyue Wei, Zhaolong Open Life Sci Topical Issue on Precision Medicine OBJECTIVE: To evaluate the practical effectiveness of smart metal artifact reduction (SMAR) in reducing artifacts caused by metallic implants. METHODS: Patients with metal implants underwent computed tomography (CT) examinations on high definition CT scanner, and the data were reconstructed with adaptive statistical iterative reconstruction (ASiR) with value weighted to 40% and smart metal artifact reduction (SMAR) technology. The comparison was assessed by both subjective and objective assessment between the two groups of images. In terms of subjective assessment, three radiologists evaluated image quality and assigned a score for visualization of anatomic structures in the critical areas of interest. Objectively, the absolute CT value of the difference (ΔCT) and artifacts index (AI) were adopted in this study for the quantitative assessment of metal artifacts. RESULTS: In subjective image quality assessment, three radiologists scored SMAR images higher than 40% ASiR images (P<0.01) and the result suggested that visualization of critical anatomic structures around the region of the metal object was significantly improved by using SMAR compared with 40% ASiR. The ΔCT and AI for quantitative assessment of metal artifacts showed that SMAR appeared to be superior for reducing metal artifacts (P<0.05) and indicated that this technical approach was more effective in improving the quality of CT images. CONCLUSION: A variety of hardware (dental filling, embolization coil, instrumented spine, hip implant, knee implant) are processed with the SMAR algorithm to demonstrate good recovery of soft tissue around the metal. This artifact reduction allows for the clearer visualization of structures hidden underneath. De Gruyter 2018-05-18 /pmc/articles/PMC7874696/ /pubmed/33817081 http://dx.doi.org/10.1515/biol-2018-0021 Text en © 2018 Peng Zhou et al., published by De Gruyter http://creativecommons.org/licenses/by-nc-nd/4.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. |
spellingShingle | Topical Issue on Precision Medicine Zhou, Peng Zhang, Chunling Gao, Zhen Cai, Wangshu Yan, Deyue Wei, Zhaolong Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software |
title | Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software |
title_full | Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software |
title_fullStr | Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software |
title_full_unstemmed | Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software |
title_short | Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software |
title_sort | evaluation of the quality of ct images acquired with smart metal artifact reduction software |
topic | Topical Issue on Precision Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7874696/ https://www.ncbi.nlm.nih.gov/pubmed/33817081 http://dx.doi.org/10.1515/biol-2018-0021 |
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