Application Value Of Deep Learning Reconstruction To Improve Image Quality Of Low-dose Chest CT

Objective To evaluate the effectiveness of deep learning reconstruction(DLR)compared with hybrid iterative reconstruction(HIR)in improving the image quality in chest low-dose CT(LDCT).Methods 77 patients who underwent LDCT scan for physical examination or followup in Peking Union Medical College Hospital from October 2020 to March 2021 were retrospectively included.The LDCT images were reconstructed with HIR at standard level(HIR-Std),DLR at standard and strong level(DLR-Std and DLR-Str)retrospectively.Regions of interest were placed on pulmonary lobe,aorta,subscapularis muscle and axillary fat to measure the CT value and image noise,then to calculate the signal to noise ratio(SNR)and contrast to noise ratio(CNR).Subjective image quality was evaluated using Likert 5-score method by two experienced radiologists.The number and features of ground-glass nodule(GGN)were also assessed.If the scores of the two radiologists are inconsistent,the score was determined by the third radiologist.The objective and subjective image evaluation were compared using the Kruskal-Wallis test with Bonferroni correction.Results Among HIR-Std,DLR-Std and DLR-Str images,there was no statistically significant difference in CT value of pulmonary lobe,aorta,subscapularis muscle and axillary fat(all P>0.05),but there were statistically significant differences in image noise,SNR and the CNR of pulmonary lobe,aorta,subscapularis muscle and axillary fat(all P<0.05).Compared with HIR images,DLR images had lower objective and subjective image noise,higher SNR and CNR(all P<0.05).The scores of DLR images were superior to HIR images in identifying lung fissures,pulmonary vessels,trachea and bronchi,lymph nodes,pleura,pericardium and GGN(all P<0.05).Conclusion DLR can significantly reduced the image noise of LDCT and ensure image quality at lower radiation dose levels.DLR images show GGN well.Objectives To explore the performance of low-dose computed tomography(LDCT)with deep learning reconstruction(DLR)for the improvement of image quality and assessment of lung parenchyma.Methods Sixty patients underwent chest conventional dose CT(RDCT)followed by LDCT during same patient encounter.RDCT images were reconstructed with hybrid iterative reconstruction(HIR)and LDCT images were reconstructed with HIR and DLR,both using lung algorithm.Radiation exposure was recorded.Image noise,signal-to-noise ratio,and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction.Results The effective radiation dose of LDCT was significantly lower than that of RDCT(0.29 ± 0.03 vs 2.05 ± 0.65 mSv,P<0.001).The mean image noise±standard deviation was 33.9±4.7,39.6 ±4.3 and 31.2 ± 3.2 HU in RDCT,LDCT HIR-Strong and LDCT DLR-Strong,respectively(P<0.001).The overall image quality of LDCT DLRStrong was significantly better than that of LDCT HIR-Strong(P<0.001)and comparable to that of RDCT(P>0.05).LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules,increased attenuation,linear opacity,and airway lesions(all P>0.05).The visualization of subsolid nodules and decreased attenuation was better with DLR-Strong than with HIR-Strong in LDCT but inferior to RDCT(all P<0.05).Conclusion LDCT DLR can effectively reduce image noise and improve image quality.LDCT-DLR provides good performance for evaluating pulmonary lesions,except for subsolid nodules and decreased lung attenuation,compared to RDCT,with a nearly 86%reduction in radiation.