JOURNAL ARTICLE
Evaluation of occupational radiation exposure and influencing factors for healthcare workers during diagnostic computed tomography imaging.
Published In: Radiation Protection Dosimetry, 2025, v. 201, n. 7. P. 522 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Nagamoto, Keisuke; Kawachino, Tomonori; Suetsugu, Yoshiki; Urasaki, Reika; Tokumoto, Saki; Kohzaki, Masaoki; Nihei, Shun-ichi; Okazaki, Ryuji 3 of 3
Abstract
This study investigates factors influencing radiation exposure to healthcare workers assisting during diagnostic computed tomography (CT) imaging and proposes measures to minimize their effective dose. Measurements of personal dose equivalent Hp(10) were taken from nine professionals (five intensivists and four radiological technologists) across 112 CT procedures, categorizing tasks by proximity to the CT gantry. Results identified dose–length product (DLP) and tube current as significant predictors of effective dose, with higher doses observed during gantry-proximal tasks and among intensivists due to higher DLP imaging protocols. The use of lead aprons reduced exposure by approximately 89%, and recommendations include implementing low-dose imaging protocols, optimizing scan coverage, and consistent use of protective equipment to enhance occupational radiation safety. Although effective doses remained below regulatory limits, the study emphasizes the importance of cumulative exposure management and tailored radiation protection strategies based on professional roles and task proximity.
Additional Information
- Source:Radiation Protection Dosimetry. 2025/05, Vol. 201, Issue 7, p522
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:01448420
- DOI:10.1093/rpd/ncaf049
- Accession Number:186054228
- Copyright Statement:Copyright of Radiation Protection Dosimetry is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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