深度学习重建对增强CT胆系图像质量的影响

目的 通过比较多种重建算法,探讨深度学习重建(DLR)对增强CT上胆道系统图像质量的影响。方法 回顾性纳入30例本院进行增强CT检查并伴有胆总管或肝外胆管扩张的患者,分别采用滤波反投影算法(FBP)、三维自适应迭代(AIDR 3D)、全模型迭代算法(FIRST)和DLR对门脉期图像进行重建。比较4组图像信号噪声比(SNR)、对比噪声比(CNR)及噪声,对4组图像质量进行主观评价排序并比较。结果 除AIDR 3D肝实质CNR外,DLR图像的CNR(胆管:4.42±0.87,肝实质:3.78±1.47)显著高于FBP[胆管:2.21±1.02(P<0.001),肝实质:1.43±1.29(P<0.001)]、AIDR 3D[胆管:2.81±0.91(P=0.024),肝实质:2.39±1.94(P=0.278)]及FIRST[胆管:2.51±1.24(P<0.001),肝实质:2.45±1.81(P=0.003)],DLR图像的SNR(胆管:1.39±0.85,肝实质:9.75±1.90)显著高于FBP[胆管:0.86±0.63 (P<0.001),肝实质:3.31±1.12 (P<0.001)]和 FIRST[胆管:1.01±0.61(P=0.013),肝实质:5.73±1.37 (P<0.001)],DLR图像的噪声(10.51±3.53)显著低于FBP[24.10±3.92 (P<0.001)]、AIDR 3D[15.72±2.41 (P=0.032)]和 FIRST[17.20±3.82 (P<0.001)]。DLR图像的主观评价排序[4(4,4)分]显著高于FPB[1(1,1)分](P<0.001)、AIDR 3D[3(2,3)分](P=0.029)和FIRST[2(2,3)分](P<0.001)。结论 深度学习重建可提高增强CT图像质量,有助于更好地观察胆道系统。

Effect of Deep Learning-based Contrast-enhanced CT Image Reconstruction on the Image Quality of the Biliary System

Objective To evaluate the effect of a deep learning reconstruction (DLR) method on the visibility of contrast-enhanced CT images of the biliary system by comparing it with different iterative reconstruction algorithms including the adaptive iterative dose reduction 3D (AIDR 3D) algorithm,forward projected model based iterative reconstruction solution (FIRST),and filtered back projection (FBP) algorithm. Methods A total of 30 patients subjected to abdominal contrast-enhanced CT and diagnosed with dilatation of common bile duct or extrahepatic bile duct were retrospectively included in this study.The images of the portal phase were reconstructed via four different algorithms (FBP,AIDR 3D,FIRST,and DLR).Signal to noise ratio (SNR) and contrast to noise ratio (CNR) of the dilated bile duct,liver parenchyma,measurable bile duct lesions,and image noise were compared between the four datasets.In subjective analyses,two radiologists independently scored the image quality (best:4 points,second:3 points;third:2 points;fourth:1 point) of the four datasets based on the noise and image visual quality of the biliary system.The Friedman and the Bonferroni-Dunn post-hoc tests were performed for comparison. Results The DLR images (bile duct:4.42±0.87;liver parenchyma:3.78±1.47) yielded higher CNR than the FBP (bile duct:2.21±1.02,P<0.001;liver parenchyma:1.43±1.29,P<0.001),AIDR 3D (bile duct:2.81±0.91,P=0.024;liver parenchyma:2.39±1.94,P=0.278),and FIRST (bile duct:2.51±1.24,P<0.001;liver parenchyma:2.45±1.81,P=0.003) images.Furthermore,the DLR images had higher SNR (bile duct:1.39±0.85,liver parenchyma:9.75±1.90) than the FBP (bile duct:0.86±0.63,P<0.001;liver parenchyma:3.31±1.12,P<0.001) and FIRST (bile duct:1.01±0.61,P=0.013;liver parenchyma:5.73±1.37,P<0.001) images,and showed lower noise (10.51±3.53) than the FBP(4.10±3.92,P<0.001),AIDR 3D (15.72±2.41,P=0.032),and FIRST (17.20±3.82,P<0.001) images.SNR and CNR showed no significant differences between FIRST and AIDR 3D images (all P>0.05).DLR images [4(4,4)] obtained higher score than FPB [1(1,1),P<0.001],AIDR3D[3 (2,3),P=0.029],and FIRST[2 (2,3),P<0.001] images. Conclusion DLR algorithm improved the subjective and objective quality of the contrast-enhanced CT image of the biliary system.