Wang Shi-Tian, Xu Jia, Wang Xuan, Wang Yun, Xue Hua-Dan, Jin Zheng-Yu
Department of Radiology,PUMC Hospital,CAMS and PUMC,Beijing 100730,China.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2022 Aug;44(4):614-620. doi: 10.3881/j.issn.1000-503X.14818.
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,<0.001;liver parenchyma:1.43±1.29,<0.001),AIDR 3D (bile duct:2.81±0.91,=0.024;liver parenchyma:2.39±1.94,=0.278),and FIRST (bile duct:2.51±1.24,<0.001;liver parenchyma:2.45±1.81,=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,<0.001;liver parenchyma:3.31±1.12,<0.001) and FIRST (bile duct:1.01±0.61,=0.013;liver parenchyma:5.73±1.37,<0.001) images,and showed lower noise (10.51±3.53) than the FBP(4.10±3.92,<0.001),AIDR 3D (15.72±2.41,=0.032),and FIRST (17.20±3.82,<0.001) images.SNR and CNR showed no significant differences between FIRST and AIDR 3D images (all >0.05).DLR images [4(4,4)] obtained higher score than FPB [1(1,1),<0.001],AIDR3D[3 (2,3),=0.029],and FIRST[2 (2,3),<0.001] images. Conclusion DLR algorithm improved the subjective and objective quality of the contrast-enhanced CT image of the biliary system.
目的 通过将深度学习重建（DLR）方法与不同的迭代重建算法（包括自适应迭代剂量降低3D（AIDR 3D）算法、基于前向投影模型的迭代重建解决方案（FIRST）和滤波反投影（FBP）算法）进行比较，评估其对胆管系统对比增强CT图像可视性的影响。方法 本研究回顾性纳入了30例接受腹部对比增强CT检查且诊断为胆总管或肝外胆管扩张的患者。通过四种不同算法（FBP、AIDR 3D、FIRST和DLR）重建门静脉期图像。比较四个数据集中扩张胆管、肝实质、可测量胆管病变的信噪比（SNR）和对比噪声比（CNR）以及图像噪声。在主观分析中，两名放射科医生基于胆管系统的噪声和图像视觉质量，对四个数据集的图像质量（最佳：4分；第二：3分；第三：2分；第四：1分）进行独立评分。采用Friedman检验和Bonferroni-Dunn事后检验进行比较。结果 DLR图像（胆管：4.42±0.87；肝实质：3.78±1.47）的CNR高于FBP（胆管：2.21±1.02，<0.001；肝实质：1.43±
