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

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Authors

Jo, Gyeong Deok; Ahn, Chulkyun; Hong, Jung Hee; Kim, Da Som; Park, Jongsoo; Kim, Hyungjin; Kim, Jong Hyo; Goo, Jin Mo; Nam, Ju Gang

Issue Date
2023-09-11
Publisher
BMC
Citation
BMC Medical Imaging, Vol.23(1):121
Keywords
Artificial intelligenceDeep-learning image reconstructionNoise reductionLow-dose chest CTNodule detection
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.
ISSN
1471-2342
Language
English
URI
https://hdl.handle.net/10371/195571
DOI
https://doi.org/10.1186/s12880-023-01081-8
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