Reducing radiation dose for NN-based COVID-19 detection in helical chest CT using real-time monitored reconstruction
© 2023 Published by Elsevier Ltd.
| Veröffentlicht in: | Expert systems with applications. - 1999. - 229(2023) vom: 01. Nov., Seite 120425 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | Expert systems with applications |
| Schlagworte: | Journal Article Automated control COVID-19 Dose reduction Helical chest CT Monitored reconstruction Neural network |
| Zusammenfassung: | © 2023 Published by Elsevier Ltd. 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 |
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| Beschreibung: | Date Revised 19.06.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2023.120425 |