Characterizing imaging radiation risk in a population of 8918 patients with recurrent imaging for a better effective dose.
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2024-03
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An updated extension of effective dose was recently introduced, namely relative effective dose ( Er ), incorporating age and sex factors. In this study we extended Er application to a population of about 9000 patients who underwent multiple CT imaging exams, and we compared it with other commonly used radiation protection metrics in terms of their correlation with radiation risk. Using Monte Carlo methods, Er , dose-length-product based effective dose ( EDLP ), organ-dose based effective dose ( EOD ), and organ-dose based risk index ( RI ) were calculated for each patient. Each metric's dependency to RI was assessed in terms of its sensitivity and specificity. Er showed the best sensitivity, specificity, and agreement with RI (R2 = 0.97); while EDLP yielded the lowest specificity and, along with EOD , the lowest sensitivity. Compared to other metrics, Er provided a closer representation of patient and group risk also incorporating age and sex factors within the established framework of effective dose.
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Ria, Francesco, Madan M Rehani and Ehsan Samei (2024). Characterizing imaging radiation risk in a population of 8918 patients with recurrent imaging for a better effective dose. Scientific reports, 14(1). p. 6240. 10.1038/s41598-024-56516-1 Retrieved from https://hdl.handle.net/10161/30366.
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Francesco Ria
Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.
Dr. Ria is a member of the American Association of Physicists in Medicine task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the American Association of Physicists in Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.
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