Study on Application Value of ClearInfinity Algorithm Based on Deep Learning in Low-dose
CT Scanning of Liver Lesions
HOU Ping1,2a, LIU Jie2a, CHEN Yan2a, GAO Jianbo2a, LI Dianyuan2b
1. School of Basic Medical Sciences, Zhengzhou University, Zhengzhou Henan 450001, China; 2. a. Department of Radiology;
b. Department of Radiotherapy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan 450052, China
Abstract：Objective To explore the application value of ClearInfinity(CI) algorithm based on deep learning to improve image quality
in low-dose CT scanning of liver lesions. Methods 65 patients with hepatic disease who underwent contrast-enhanced liver CT in our
hospital were retrospectively collected. Conventional dose protocol was used in arterial phase (AP) and portal phase (PP), and low
dose protocol of abdominal three-phase enhanced scanning was used in delayed phase (DP). According to the different reconstruction
methods, iterative algorithm (50% ClearView, 50% CV) was used to reconstruct the conventional dose group (group A) in arterial
phase. The delayed low-dose group (group B) was divided into two subgroups: group B1 and group B2. Group B1 was reconstructed
by iterative algorithm (50% CV), and group B2 was reconstructed by deep learning algorithm (50% CI). The difference of radiation
dose of group A and group B were recorded and compared. The CT values of liver parenchyma, spleen and kidney in groups B1 and
B2 and the subcutaneous fat noise in groups A and B were measured, and the signal to noise ratio (SNR) and the contrast to noise
ratio (CNR) of all region of interest (ROI) were calculated. Results Compared with group A, the radiation dose of group B was
reduced by 73.3%, and the difference was statisticallysignificant (P<0.05). Comparison of noise among groups: the noise of group
B1 was the highest, and that of group B2 < group A < group B1, but there was no statistically significant differences between group
A and B2 (P=0.625). CT values of liver, pancreas and kidney were basically stable between groups B1 and B2, with no statistically
significant differences (P>0.05). The SNR and CNR of all ROIs in group B2 were all higher than those of group B1 (P<0.001).
Subjective image scores of difference groups were statistically different (P<0.001). The scores of group B1were <3 scores, which
could not meet the needs of diagnosis. While there were no significant differences in the subjective scores between group B2 and
group A (P>0.05). Conclusion When the radiation dose was greatly reduced, the 50%CI algorithm based on deep learning could
reduce the image noise of low-dose CT liver scanning, improve the image quality and satisfy diagnostic requirement.
侯平1,2a，刘杰2a，陈岩2a，高剑波2a，李甸源2b. 基于深度学习的ClearInfinity算法在
肝脏病变低剂量CT扫描中的应用价值[J]. 中国医疗设备, 2023, 38(5): 120-124.
HOU Ping1,2a, LIU Jie2a, CHEN Yan2a, GAO Jianbo2a, LI Dianyuan2b. Study on Application Value of ClearInfinity Algorithm Based on Deep Learning in Low-dose
CT Scanning of Liver Lesions. China Medical Devices, 2023, 38(5): 120-124.
