Computed Tomography Doses Calculation: Do We Really Need a New Dose Assessment Tool?

J Clin Med

Neuroinformatics and Artificial Intelligence Laboratory, Department of Neurophysiology, Neuropsychology and Neuroinformatics, Medical University of Gdansk, 80-210 Gdansk, Poland.

Published: February 2025

The increasing use of computed tomography (CT) scans significantly contributes to population exposure to ionizing radiation. Traditional dose metrics, such as dose-length product (DLP) and effective dose (ED), lack precision in reflecting individual radiation exposure. This study introduces a novel parameters such as size-specific effective dose (EDss) and the size-specific dose-length product (DLPss), to improve patient-specific dose estimation. The aim of this study is to enhance dose calculation accuracy, optimize CT protocols, and guide the development of next-generation CT technologies. A retrospective analysis of 247 abdominal and pelvic CT scans (113 women, 134 men) was conducted. Anthropometric parameters, including body mass index (BMI), cross-sectional dimensions, and dose indices, were measured. EDss and DLPss were calculated using size-specific correction factors, and statistical correlations between these parameters were assessed. The mean BMI was 25.92 ± 5.34. DLPss values ranged from 261.63 to 1217.70 mGy·cm (mean: 627.83 ± 145.32) and were roughly 21% higher than traditional DLP values, with men showing slightly higher mean values than women. EDss values ranged from 6.65 to 15.45 mSv (mean: 9.42 ± 2.18 mSv), approximately 22% higher than traditional ED values, demonstrating improved individualization. Significant correlations were observed between BMI and effective diameter (r = 0.78), with stronger correlations in men (r = 0.85). The mean CTDIvol was 11.37 ± 3.50 mGy, and SSDE averaged 13.91 ± 2.39 mGy. Scan length reductions were observed in 53.8% of cases, with statistically significant differences by gender. EDss and DLPss offer improved accuracy in radiation dose estimation, addressing the limitations of traditional methods. Their adoption into clinical protocols, supported by AI-driven automation, could optimize diagnostic safety and significantly reduce radiation risk for patients. Further multicenter studies and technological advancements are recommended to validate these metrics and facilitate their integration into daily practice.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856821PMC
http://dx.doi.org/10.3390/jcm14041348DOI Listing

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