Ultra-low-dose chest CT combined with deep learning reconstruction can be used for pulmonary nodule assessment
FAN Qiuju
WU Haibo
TAN Hui
GUO Yanbing
MA Guangming
YU Nan
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.
Keywords：pulmonary nodulesultra-low dosedeep learning reconstructionimage quality
Publication Date：2024-11-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1189-1194 )