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75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
DC Field | Value | Language |
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dc.contributor.author | Jo, Gyeong Deok | - |
dc.contributor.author | Ahn, Chulkyun | - |
dc.contributor.author | Hong, Jung Hee | - |
dc.contributor.author | Kim, Da Som | - |
dc.contributor.author | Park, Jongsoo | - |
dc.contributor.author | Kim, Hyungjin | - |
dc.contributor.author | Kim, Jong Hyo | - |
dc.contributor.author | Goo, Jin Mo | - |
dc.contributor.author | Nam, Ju Gang | - |
dc.date.accessioned | 2023-09-18T05:43:02Z | - |
dc.date.available | 2023-09-18T14:44:02Z | - |
dc.date.issued | 2023-09-11 | - |
dc.identifier.citation | BMC Medical Imaging, Vol.23(1):121 | ko_KR |
dc.identifier.issn | 1471-2342 | - |
dc.identifier.uri | https://hdl.handle.net/10371/195571 | - |
dc.description.abstract | 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. | ko_KR |
dc.description.sponsorship | This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (RS-2020-KD000226, 1711174549) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1A2C1091805).
The funder had no role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the article for publication. | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BMC | ko_KR |
dc.subject | Artificial intelligence | - |
dc.subject | Deep-learning image reconstruction | - |
dc.subject | Noise reduction | - |
dc.subject | Low-dose chest CT | - |
dc.subject | Nodule detection | - |
dc.title | 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT | ko_KR |
dc.type | Article | ko_KR |
dc.identifier.doi | 10.1186/s12880-023-01081-8 | ko_KR |
dc.citation.journaltitle | BMC Medical Imaging | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | BioMed Central Ltd., part of Springer Nature | - |
dc.date.updated | 2023-09-17T03:09:45Z | - |
dc.citation.volume | 23 | ko_KR |
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