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• W4376274370 abstract "Computed tomography is a powerful tool for medical examination, which plays a particularly important role in the investigation of acute diseases, such as COVID-19. A growing concern in relation to CT scans is the radiation to which the patients are exposed, and a lot of research is dedicated to methods and approaches to how to reduce the radiation dose in X-ray CT studies. In this paper, we propose a novel scanning protocol based on real-time monitored reconstruction for a helical chest CT using a pre-trained neural network model for COVID-19 detection as an expert. In a simulated study, for the first time, we proposed using per-slice stopping rules based on the COVID-19 detection neural network output to reduce the frequency of projection acquisition for portions of the scanning process. The proposed method allows reducing the total number of X-ray projections necessary for COVID-19 detection, and thus reducing the radiation dose, without a significant decrease in the prediction accuracy. The proposed protocol was evaluated on 163 patients from the COVID-CTset dataset, providing a mean dose reduction of 15.1% while the mean decrease in prediction accuracy amounted to only 1.9% achieving a Pareto improvement over a fixed protocol." @default.
• W4376274370 created "2023-05-13" @default.
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• W4376274370 date "2023-11-01" @default.
• W4376274370 modified "2023-09-29" @default.
• W4376274370 title "Reducing radiation dose for NN-based COVID-19 detection in helical chest CT using real-time monitored reconstruction" @default.
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• W4376274370 doi "https://doi.org/10.1016/j.eswa.2023.120425" @default.
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