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• W2897587478 abstract "This paper tackles the problem of estimating exposure to static magnetic field (SMF) in magnetic resonance imaging (MRI) sites using a non‐invasive approach. The proposed approach relies on a vision‐based system to detect people's body parts and on a mathematical model to compute SMF exposure. A multi‐view camera system was used to capture the MRI room, and a vision‐based system was applied to detect body parts. The detected localization was then fed into a mathematical model to compute SMF exposure. In this study, we focused on exposure at the neck due to two main reasons. First, according to regulations, the limit of exposure at head and trunk for MR workers is higher than that for the general public. Second, it was easier to attach a dosimeter at the neck to perform measurements, which allowed a quantitative evaluation of our approach. This approach was applied to two scenarios simulating the daily movements of medical workers for which dosimeter measurements were also recorded. The results indicated that the proposed approach predicted occupational SMF exposure with reasonable accuracy compared with the dosimeter measurements. The proposed approach is a simple safe working procedure to estimate the exposure of MR workers at different parts of the body without wearing any marker detection. It can be applied to reduce occupational SMF exposure, without changes in workers’ performances. For that reason, our non‐invasive proposed method can be used as a simple safety tool to estimate occupational SMF exposure in MR sites. Bioelectromagnetics. 39:503–515, 2018.© 2018 Wiley Periodicals, Inc." @default.
• W2897587478 created "2018-10-26" @default.
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• W2897587478 date "2018-10-01" @default.
• W2897587478 modified "2023-10-14" @default.
• W2897587478 title "Evaluation of occupational exposure to static magnetic field in MRI sites based on body pose estimation and SMF analytical computation" @default.
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• W2897587478 doi "https://doi.org/10.1002/bem.22145" @default.
• W2897587478 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30307039" @default.
• W2897587478 hasPublicationYear "2018" @default.
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