Objective To analyze the effects of deep learning image reconstruction(DLIR)and adaptive statistical iterative recon-struction V(ASIR-V)on the imaging quality of chest CT in patient with pulmonary nodules,and to evaluate the differences based on different image reconstruction techniques in the detection of efficiency of computer-aided diagnosis(CAD)for pulmonary nodules.Methods The image data of pulmonary nodules of eighty patients with chest CT screening were reconstructed with ASIR-V 80％,DLIR-low(DLIR-L),DLIR-medium(DLIR-M)and DLIR-high(DLIR-H)images,respectively.The objective image quality and sub-jective image quality of the four groups were compared and analyzed.Objective image quality includes CT value of region of interest(ROI),noise,signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR)and image average gradient.The diagnostic efficacy of CAD in detecting pulmonary nodules of reconstructed images among four groups were further evaluated.Results There were no signifi-cant difference in CT value of ROI of reconstructed images among the four groups(P＞0.05).The noise,SNR and CNR of DLIR-H images were similar to those of ASIR-V 80％(P＞0.05),but significantly better than those of DLIR-L and DLIR-M(P＜0.05).The average gradient of DLIR-L,DLIR-M and DLIR-H images were significantly higher than those of ASIR-V 80％(P＜0.05).The subjective image quality scores of DLIR-L,DLIR-M and DLIR-H images were significantly higher than those of ASIR-V 80％(P＜0.05),and the subjective image quality score of DLIR-H image was the highest.CAD showed the highest true positive rate in DLIR-H images for detecting pulmonary nodules(P＜0.05),and CAD showed the highest false positives per capita in ASIR-V 80％images for detecting pulmonary nodules(P＜0.05).Conclusion The noise,SNR and CNR of DLIR-H images are similar to those of ASIR-V 80％,with the significantly higher image clarity and subjective image quality scores.DLIR-H has advantages in CAD detection of pulmonary nodules,which is an ideal image reconstruction technology for chest CT pulmonary nodule screening.