Impact of pixel shine algorithm based on deep machine learning on image quality of abdominal low-dose plain CT scanning in patients with high body mass index / 中国医学影像技术

ABSTRACT

Objective To investigate the impact of deep machine learning Pixel Shine (PS) algorithm on image quality of abdominal low-dose plain CT scanning in BMI≥25 kg/m2 patients.Methods A total of 59 patients (BMI≥25 kg/m2) who underwent abdominal CT scan were collected.The patients were divided into group A (100 kVp,n=30) and B (120 kVp,n=29) according to the tube voltage.According to different reconstruction algorithms and treatment methods,patients in group A were divided into A1 (FBP),A2 (FBP+PS),A3 (50％ASiR-V) and A4 (50％ASiR-V+PS) subgroups,while in group B were divided into B1 (FBP) and B2 (50％ASiR-V) subgroups.CT and SD values of right hepatic lobe and right erector spinae were measured,then SNR and CNR of liver and CT dose index of volume (CTDIvol) were calculated.The consistency of parameters measured by two observers was evaluated.Results The consistency of parameters measured by two observers was good (all ICC＞0.80).There was no statistical difference of CT values of liver and erector among A1-A4 subgroups (all P＞0.05),whereas statistical differences of SD values of liver and erector spinae,also of SNR and CNR of liver were found (all P＜0.001).Among A1-A4 subgroups,SDA4 ＜SDA2 ＜SDA3 ＜SDA1,SNRA4 ＞SNRA2 ＞SNRA3 ＞ SNRA1 (all P＜0.001) was observed.There was no significant difference of CNR between A1 and A3 subgroup (P=0.078),while CNRA4＞ CNRA2＞ CNRA3 or CNRA1 (P＜0.001) was noticed.SD values of the liver in subgroup A2 was lower than subgroup B1,and A4 was lower than B2 subgroup (all P＜0.001),and SNR and CNR increased significantly in A2 and A4 subgroups (all P＜0.001).CTDIvol of group A was lower than that of group B (P＜0.001).Conclusion Deep machine learning PS algorithm can improve image quality of abdominal low-dose plain CT scanning in high-BMI patients.