为评估超低剂量（ULD）计算机断层扫描以及一种基于人工智能的新型重建去噪方法在肺癌筛查中的应用，本前瞻性研究纳入了123名患者，84名（70.6％）男性，平均年龄62.6 ± 5.35岁（55-75岁），其中进行了低剂量和超低剂量扫描。采用一种使用独特感知丢失进行训练的完全卷积网络进行去噪。用于提取感知特征的网络是通过对堆叠自动编码器进行去噪形式上的非监督方式进行训练的。感知特征是来自网络不同层的特征映射的组合，而不是使用单一层进行训练。两名检查医师独立评价所有图像集。ULD将平均辐射剂量降低了76％（48％-85％）。当比较阴性和需进一步诊断的Lung-RADS类别时，dULD和LD之间没有差异（ p = 0.22 RE，p> 0.999 RR），ULD和LD扫描之间也没有差异（ p = 0.75 RE，p> 0.999 RR ）。检查医师的ULD阴性似然比（LR）为0.033-0.097。dULD表现更好，阴性LR为0.021-0.051。在dULD扫描中，88（74％）和81（68％）名患者出现冠状动脉钙化（CAC），在ULD中，74（62.2％）和77（64.7％）名患者出现CAC。dULD表现出高灵敏度，为93.9％-97.6％，准确度为91.7％。LD（ICC = 0.924），dULD（ICC = 0.903）和ULD（ICC = 0.817）扫描的CAC得分读者间达成了几乎完全的一致。一种新型基于人工智能的去噪方法可实现大幅降低辐射剂量，而不会误诊像主动脉瘤这样危及生命的发现。版权所有©2023 The Association of University Radiologists。由Elsevier Inc.出版。保留所有权利。

To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer.This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images.ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p = 0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p = 0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC = 0.924), dULD (ICC = 0.903), and for ULD (ICC = 0.817) scans.A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.