分子影像学杂志2024，Vol.47Issue(11)：1189-1194,6.DOI:10.12122/j.issn.1674-4500.2024.11.06
超低剂量胸部CT结合深度学习重建可用于肺结节评估
Ultra-low-dose chest CT combined with deep learning reconstruction can be used for pulmonary nodule assessment
摘要
Abstract
Objective To evaluate the feasibility of using deep learning reconstruction(DLIR)for pulmonary nodule assessment under ultra-low dose CT(ULDCT)scanning.Methods A total of 142 patients who underwent CT scans for pulmonary nodules re-examination included.All patients were examined by both standard-dose CT(SDCT)and ULDCT.SDCT images were reconstructed with adaptive statistical iterative reconstruction-V 40%(ASIR-V40%),ULDCT images were reconstructed with ASIR-V40%and DLIR-H,respectively.A total of three sets of images were obtained(Group A,group B,group C).The radiation dose of both scanning modes and the number of lung nodules were recorded manually.The CT values and noise values(SD)of lung tissue,aorta and muscle were measured in 3 groups images,and the signal-to-noise ratio(SNR)was calculated for each tissue.The malignant signs of lung nodules in the three groups were scored by double-blind method.Using the pathological diagnosis as the gold standard,the diagnostic efficacy of ULDCT and SDCT examination on the malignant signs(burr,lobular,pleural traction sign,vacuole or void,vascular perforation)of pulmonary nodules was analyzed by comparison.Statistical analysis was performed on the quantitative indicators and subjective scores of these three sets of images.Results The radiation dose of ULDCT was reduced by about 92.7%compared with SDCT,and the difference was statistically significant(P<0.05).The SD values of lung tissue,aorta and muscle in group C were lower than those in group B,and the SNR was higher than that in group B(P<0.05),and the ability to display malignant signs of nodules were better than those in group B,and there was no statistical difference between group C and group A(P>0.05).The number of pulmonary nodules detected in the three groups was 187,179 and 187,respectively.Compared with the pathological results,the efficacy of group A and group C in diagnosing malignant pulmonary nodules was higher than that of group B,and the difference was statistically significant(P<0.05).Conclusion Ultra-low-dose chest CT combined with deep learning reconstruction can obtain image quality comparable to ASIR-V40%of SDCT,and show good detection and signs of nodules,which can be used for clinical evaluation of pulmonary nodules.关键词
肺结节/超低剂量/深度学习重建/图像质量Key words
pulmonary nodules/ultra-low dose/deep learning reconstruction/image quality引用本文复制引用
樊秋菊,吴海波,谭辉,郭炎兵,马光明,于楠..超低剂量胸部CT结合深度学习重建可用于肺结节评估[J].分子影像学杂志,2024,47(11):1189-1194,6.基金项目
陕西省教育厅青年创新团队科研计划项目(23JP035、23JP036) （23JP035、23JP036）
咸阳市重点研发计划项目(L2023-ZDYF-SF-048) （L2023-ZDYF-SF-048）
