Reducing radiation dose for NN-based COVID-19 detection in helical chest CT using real-time monitored reconstruction.
Reducing radiation dose for NN-based COVID-19 detection in helical chest CT using real-time monitored reconstruction.
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DOI：
10.1016/j.eswa.2023.120425
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发表时间：
2023-11-01
影响因子：
8.5
通讯作者：
Arlazarov, Vladimir V.
中科院分区：
文献类型：
--
作者：
Bulatov, Konstantin B.;Ingacheva, Anastasia S.;Gilmanov, Marat I.;Chukalina, Marina V.;Nikolaev, Dmitry P.;Arlazarov, Vladimir V.
计算机断层扫描是医学检查的有力工具，在新冠肺炎等急性疾病的调查中发挥着尤为重要的作用。与CT扫描相关的一个日益令人担忧的问题是患者所暴露的辐射，许多研究致力于如何在X射线CT研究中减少辐射剂量的方法和途径。在本文中，我们提出了一种新的扫描协议，该协议基于实时监控重建的螺旋胸部CT使用预先训练的新冠肺炎检测神经网络模型作为专家。在模拟研究中，我们首次提出使用基于新冠肺炎检测神经网络输出的逐切片停止规则来降低部分扫描过程的投影获取频率。该方法可以减少新冠肺炎探测所需的X射线投影总数，从而在不显著降低预测精度的情况下减少辐射剂量。在COVID-CTSET数据集的163名患者中对建议的方案进行了评估，提供了15.1%的平均剂量减少，而预测精度的平均下降仅为1.9%，实现了比固定方案更好的帕累托改进。
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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影响因子：
19.7
作者：
POLACIN, A;KALENDER, WA;MARCHAL, G
通讯作者：
MARCHAL, G
影响因子：
3.9
作者：
Bulatov, Konstantin;Chukalina, Marina;Arlazarov, Vladimir V.
通讯作者：
Arlazarov, Vladimir V.
受监测的断层造影重建 - 一个先进的工具，用于研究纳米材料的3D形态。
DOI：
10.3390/nano11102524
发表时间：
2021-09-27
期刊：
Nanomaterials (Basel, Switzerland)
影响因子：
--
作者：
Bulatov K;Chukalina M;Kutukova K;Kohan V;Ingacheva A;Buzmakov A;Arlazarov VV;Zschech E
通讯作者：
Zschech E
影响因子：
7.7
作者：
Amyar A;Modzelewski R;Li H;Ruan S
通讯作者：
Ruan S
影响因子：
4.6
作者：
Hagen, Charlotte K.;Vittoria, Fabio A.;Olivo, Alessandro
通讯作者：
Olivo, Alessandro