Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis

• Published in: European radiology 2024-11
• Main Authors: Kobayashi, Naoki, Nakaura, Takeshi, Yoshida, Naofumi, Nagayama, Yasunori, Kidoh, Masafumi, Uetani, Hiroyuki, Sakabe, Daisuke, Kawamata, Yuki, Funama, Yoshinori, Tsutsumi, Takashi, Hirai, Toshinori
• Format: Article
• Language: English
• ISSN: 1432-1084; 1432-1084
• DOI: 10.1007/s00330-024-11212-6

The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data. We retrospectively analyzed scan data of adult patients who underwent body CT during two periods relative to DLR implementation at our facility: a 12-month pre-DLR phase (n = 5553) using hybrid iterative reconstruction and a 12-month post-DLR phase (n = 5494) with routine CT reconstruction transitioning to DLR. To ensure comparability between two groups, we employed propensity score matching 1:1 based on age, sex, and body mass index. Dose data were collected to estimate organ-specific equivalent doses and total effective doses. We assessed the average dose reduction post-DLR implementation and estimated the Lifetime Attributable Risk (LAR) for cancer per CT exam pre- and post-DLR implementation. The number of radiation-induced cancers before and after the implementation of DLR was also estimated. After propensity score matching, 5247 cases from each group were included in the final analysis. Post-DLR, the total effective body CT dose significantly decreased to 15.5 ± 10.3 mSv from 28.1 ± 14.0 mSv pre-DLR (p