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75% radiation dose reduction using deep learning reconstruction on low-dose chest CT

OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-l...

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Autores principales: Jo, Gyeong Deok, Ahn, Chulkyun, Hong, Jung Hee, Kim, Da Som, Park, Jongsoo, Kim, Hyungjin, Kim, Jong Hyo, Goo, Jin Mo, Nam, Ju Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494344/
https://www.ncbi.nlm.nih.gov/pubmed/37697262
http://dx.doi.org/10.1186/s12880-023-01081-8
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author Jo, Gyeong Deok
Ahn, Chulkyun
Hong, Jung Hee
Kim, Da Som
Park, Jongsoo
Kim, Hyungjin
Kim, Jong Hyo
Goo, Jin Mo
Nam, Ju Gang
author_facet Jo, Gyeong Deok
Ahn, Chulkyun
Hong, Jung Hee
Kim, Da Som
Park, Jongsoo
Kim, Hyungjin
Kim, Jong Hyo
Goo, Jin Mo
Nam, Ju Gang
author_sort Jo, Gyeong Deok
collection PubMed
description OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). MATERIALS AND METHODS: We retrospectively collected 100 patients (median age, 61 years [IQR, 53–70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). RESULTS: The median effective dose was 0.16 (IQR, 0.14–0.18) mSv for QLD CT and 0.65 (IQR, 0.57–0.71) mSv for LDCT. The radiologists’ evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06). CONCLUSION: QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01081-8.
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spelling pubmed-104943442023-09-12 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT Jo, Gyeong Deok Ahn, Chulkyun Hong, Jung Hee Kim, Da Som Park, Jongsoo Kim, Hyungjin Kim, Jong Hyo Goo, Jin Mo Nam, Ju Gang BMC Med Imaging Research OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). MATERIALS AND METHODS: We retrospectively collected 100 patients (median age, 61 years [IQR, 53–70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). RESULTS: The median effective dose was 0.16 (IQR, 0.14–0.18) mSv for QLD CT and 0.65 (IQR, 0.57–0.71) mSv for LDCT. The radiologists’ evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06). CONCLUSION: QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01081-8. BioMed Central 2023-09-11 /pmc/articles/PMC10494344/ /pubmed/37697262 http://dx.doi.org/10.1186/s12880-023-01081-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Jo, Gyeong Deok
Ahn, Chulkyun
Hong, Jung Hee
Kim, Da Som
Park, Jongsoo
Kim, Hyungjin
Kim, Jong Hyo
Goo, Jin Mo
Nam, Ju Gang
75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
title 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
title_full 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
title_fullStr 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
title_full_unstemmed 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
title_short 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
title_sort 75% radiation dose reduction using deep learning reconstruction on low-dose chest ct
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494344/
https://www.ncbi.nlm.nih.gov/pubmed/37697262
http://dx.doi.org/10.1186/s12880-023-01081-8
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