Objective To evaluate the clinical value of spectral CT monochromatic imaging in improvement of imaging quality of bronchial arteries.Methods We retrospevtively analyzed the chest CT images in 38 patients who underwent the contrast-enhanced spectral CT.These images included a set of 140 kVp polychromatic images and default 70 keV monochromatic images.Using a standard Gemstone Spectral Imaging (GSI)viewer at an advanced workstation (AW4.6),an optimal energy level (in keV)for obtai-ning the best CNR of the bronchial artery could be automatically obtained.The SNR,CNR and objective imaging quality score for these 3 imaging sets (140 kVp,70 keV and optimal energy level)were obtained and compared with one-way ANOVA .Results The optimal energy levels for obtaining the best CNR were (62.58±2.74)keV.The SNR of the 140 kVp polychromatic images,70 keV monochromatic images,and the optimal keV monochromatic images were 1 6.44±5.85,20.96 ±8.32 and 24.91 ±9.91,the CNR were 13.30±5.45,1 7.25±6.97 and 20.67±8.62,and the subjective imaging quality scores were 1.97 ±0.82,3.24±0.75 and 4.47±0.60,respectively,exhibiting significant differences among groups (F =10.1 7,10.1 7 and 1 1 1.12,P <0.00).The optimal monochromatic group was superior to the 70 keV group and the 140 kVp mixed-energy group.Conclusion Monochromatic images at approximately 62 keV in dual-energy spectral CTA yields the best CNR and highest diagnostic confidence for imaging bronchial ar-teries,which may improve imaging quality for imaging bronchial arteries.

Objective To investigate the impact of ultra-low dose CT(ULDCT)scanning combined with deep learning image reconstruction(DLIR)on quantitative analysis of pulmonary nodules using computer aided diagnostic system(CAD).Methods Fifty-six further consultation patients with pulmonary nodules were prospectively enrolled.ULDCT and standard-dose CT(SDCT)were performed.The raw ULDCT images were reconstructed using adaptive statistical iterative reconstruction-V40%(ASIR-V40%)and high-strength DLIR(DLIR-H)to obtain ULDCT-ASIR-V40%(group A)and ULDCT-DLIR-H(group B)images,while SDCT images were reconstructed with ASIR-V40%to obtain SDCT-ASIR-V40%(group C)images.Pulmonary nodules with long diameter of 4-30 mm were selected as the target nodules based on reconstructed images.The nodules were divided into solid nodules,calcified nodules and non-solid nodules by 2 physicians.CAD software was used to evaluate the classification of nodules based on 3 groups of images,and the long diameter,transverse diameter,density,volume and malignant risk were quantitatively analyzed.Results Totally 104 target nodules were selected,including 51 solid nodules,26 calcified nodules and 27 non-solid nodules according to physicians.CAD classified 53 solid,24 calcified and 27 non-solid nodules based on group A and B,while based on group C,CAD classification was consistent with that of physicians'.Compared with group C,the density of solid and calcified nodules,the volume and malignant risk of non-solid nodules judged by CAD in group A decreased,so did the density of calcified nodules in group B(all P＜0.05).No significant difference of the other CAD quantitative parameters of nodules was found among 3 groups(all P＞0.05).Conclusion ULDCT scanning combined with DLIR might underestimate the density of calcified pulmonary nodules judged by CAD,but had no significant impact on the other CAD quantitative parameters.