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Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses

BACKGROUND: Numerous computed tomography (CT) image reconstruction algorithms have been developed to improve image quality, and high-quality renal CT images are crucial to clinical diagnosis. This study evaluated the image quality and lesion visibility of deep learning-based image reconstruction (DL...

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Autores principales: Bie, Yifan, Yang, Shuo, Li, Xingchao, Zhao, Kun, Zhang, Changlei, Zhong, Hai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102763/
https://www.ncbi.nlm.nih.gov/pubmed/37064389
http://dx.doi.org/10.21037/qims-22-852
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author Bie, Yifan
Yang, Shuo
Li, Xingchao
Zhao, Kun
Zhang, Changlei
Zhong, Hai
author_facet Bie, Yifan
Yang, Shuo
Li, Xingchao
Zhao, Kun
Zhang, Changlei
Zhong, Hai
author_sort Bie, Yifan
collection PubMed
description BACKGROUND: Numerous computed tomography (CT) image reconstruction algorithms have been developed to improve image quality, and high-quality renal CT images are crucial to clinical diagnosis. This study evaluated the image quality and lesion visibility of deep learning-based image reconstruction (DLIR) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V) in contrast-enhanced renal CT at different reconstruction strengths and doses. METHODS: From January 2020 to May 2021, we prospectively included 101 patients who underwent renal contrast-enhanced CT scanning (69 at 120 kV; 32 at 80 kV). All image data were reconstructed with ASiR-V (30% and 70%) and DLIR at low, medium, and high reconstruction strengths (DLIR-L, DLIR-M, and DLIR-H, respectively). The CT number, noise, noise reduction rate (NRR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall image quality, and the proportion of acceptable images were compared. Lesions of DLIR groups were evaluated, and the conspicuity-to-noise ratio (C/N) was calculated. RESULTS: Quantitative values (noise, SNR, CNR, and NRR) significantly differed between all reconstructions at 120 and 80 kV (P<0.001) and between each reconstruction, except ASiR-V 70% vs. DLIR-M. At 120 kV, the overall image quality and the proportion of acceptable images significantly differed between all reconstructions (P<0.001) and between each reconstruction, except ASiR-V 30% vs. DLIR-L and ASiR-V 70% vs. DLIR-M. At 80 kV, the overall image quality significantly differed between all reconstructions (P<0.001) and between each reconstruction, except between ASiR-V 30%, ASiR-V 70%, and DLIR-L. Quantitative and qualitative values were highest in DLIR-H, while the values were close in DLIR-H (80 kV) vs. ASiR-V 70% (120 kV) and DLIR-M (80 kV) vs. ASiR-V 30% (120 kV). The lesion conspicuity and noise significantly differed in DLIR at 120 kV and 80 kV (P<0.001). C/N significantly differed in DLIR at 120 kV (P<0.001) but not at 80 kV. DLIR-L and DLIR-M exhibited much-improved lesion display (C/N >1), and DLIR-H exhibited much-improved noise (C/N <1) at 120 kV. CONCLUSIONS: DLIR significantly improved the image quality and lesion visibility of renal CT compared with ASiR-V, even at a low dose.
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spelling pubmed-101027632023-04-15 Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses Bie, Yifan Yang, Shuo Li, Xingchao Zhao, Kun Zhang, Changlei Zhong, Hai Quant Imaging Med Surg Original Article BACKGROUND: Numerous computed tomography (CT) image reconstruction algorithms have been developed to improve image quality, and high-quality renal CT images are crucial to clinical diagnosis. This study evaluated the image quality and lesion visibility of deep learning-based image reconstruction (DLIR) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V) in contrast-enhanced renal CT at different reconstruction strengths and doses. METHODS: From January 2020 to May 2021, we prospectively included 101 patients who underwent renal contrast-enhanced CT scanning (69 at 120 kV; 32 at 80 kV). All image data were reconstructed with ASiR-V (30% and 70%) and DLIR at low, medium, and high reconstruction strengths (DLIR-L, DLIR-M, and DLIR-H, respectively). The CT number, noise, noise reduction rate (NRR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall image quality, and the proportion of acceptable images were compared. Lesions of DLIR groups were evaluated, and the conspicuity-to-noise ratio (C/N) was calculated. RESULTS: Quantitative values (noise, SNR, CNR, and NRR) significantly differed between all reconstructions at 120 and 80 kV (P<0.001) and between each reconstruction, except ASiR-V 70% vs. DLIR-M. At 120 kV, the overall image quality and the proportion of acceptable images significantly differed between all reconstructions (P<0.001) and between each reconstruction, except ASiR-V 30% vs. DLIR-L and ASiR-V 70% vs. DLIR-M. At 80 kV, the overall image quality significantly differed between all reconstructions (P<0.001) and between each reconstruction, except between ASiR-V 30%, ASiR-V 70%, and DLIR-L. Quantitative and qualitative values were highest in DLIR-H, while the values were close in DLIR-H (80 kV) vs. ASiR-V 70% (120 kV) and DLIR-M (80 kV) vs. ASiR-V 30% (120 kV). The lesion conspicuity and noise significantly differed in DLIR at 120 kV and 80 kV (P<0.001). C/N significantly differed in DLIR at 120 kV (P<0.001) but not at 80 kV. DLIR-L and DLIR-M exhibited much-improved lesion display (C/N >1), and DLIR-H exhibited much-improved noise (C/N <1) at 120 kV. CONCLUSIONS: DLIR significantly improved the image quality and lesion visibility of renal CT compared with ASiR-V, even at a low dose. AME Publishing Company 2023-02-15 2023-04-01 /pmc/articles/PMC10102763/ /pubmed/37064389 http://dx.doi.org/10.21037/qims-22-852 Text en 2023 Quantitative Imaging in Medicine and Surgery. 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
Bie, Yifan
Yang, Shuo
Li, Xingchao
Zhao, Kun
Zhang, Changlei
Zhong, Hai
Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses
title Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses
title_full Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses
title_fullStr Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses
title_full_unstemmed Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses
title_short Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses
title_sort impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102763/
https://www.ncbi.nlm.nih.gov/pubmed/37064389
http://dx.doi.org/10.21037/qims-22-852
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