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

Content Provider	Springer Nature : BioMed Central
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
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.
Related Links	https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-023-01081-8.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14712342
DOI	10.1186/s12880-023-01081-8
Journal	BMC Medical Imaging
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-09-11
Access Restriction	Open
Subject Keyword	Imaging Radiology Artificial intelligence Deep-learning image reconstruction Noise reduction Low-dose chest CT Nodule detection
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	2.8/2023

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