深度学习图像重建结合计算机辅助诊断在肺结节CT筛查中的应用研究  

Application of deep learning image reconstruction combined with computer-aided diagnosis in CT screening of pulmonary nodules

作　　者：卢竞 李涛 韦碧妙 陈航 邹达 梁洪峰 LU Jing;LI Tao;WEI Bimiao;CHEN Hang;ZOU Da;LIANG Hongfeng(Department of Radiology,Liuzhou Workers Hospital,Liuzhou 545005,China)

机构地区：[1]柳州市工人医院放射科,广西柳州545005

出　　处：《实用放射学杂志》2024年第1期135-139,共5页Journal of Practical Radiology

摘　　要：目的分析深度学习图像重建(DLIR)、自适应统计迭代重建算法(ASIR-V)对胸部CT肺结节成像质量的影响,评估不同图像重建技术下计算机辅助诊断(CAD)对肺结节检测效能的差异。方法胸部CT肺结节筛查患者80例,分别行ASIR-V80%、DLIR-低(DLIR-L)、DLIR-中等(DLIR-M)、DLIR-高(DLIR-H)图像重建,对比分析4组图像的客观图像质量和主观图像质量,其中客观图像质量包括图像感兴趣区(ROI)的CT值、噪声、信噪比(SNR)、对比噪声比(CNR)和图像平均梯度。评估4组图像CAD检测肺结节的诊断效能。结果4组图像相同ROI的CT值无统计学差异(P>0.05)。DLIR-H图像的噪声、SNR、CNR与ASIR-V80%相当(P>0.05),优于DLIR-L与DLIR-M(P<0.05)。DLIR-L、DLIR-M、DLIR-H图像的平均梯度均高于ASIR-V80%(P<0.05)。DLIR-L、DLIR-M、DLIR-H图像的主观图像质量评分均高于ASIR-V80%(P<0.05),DLIR-H图像的主观图像质量评分最高。DLIR-H图像CAD检测肺结节的真阳性率最高(P<0.05),ASIR-V80%图像CAD检测肺结节的人均假阳性数最高(P<0.05)。结论DLIR-H图像的噪声、SNR、CNR与ASIR-V80%相当,但图像清晰度和主观图像质量评分更高,在CAD检测肺结节时亦有优势,是目前较为理想的胸部CT肺结节筛查图像重建技术。Objective To analyze the effects of deep learning image reconstruction(DLIR)and adaptive statistical iterative reconstruction 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 subjective 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 significant difference in CT value of ROI of reconstructed images among the four groups(P>0.o5).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 pulmona

关 键 词：深度学习图像重建 自适应统计迭代重建算法 肺结节筛查 计算机辅助检测 计算机体层成像 图像质量 

分 类 号：R445.2[医药卫生—影像医学与核医学] R814.42[医药卫生—诊断学] R563[医药卫生—临床医学]