摘要
Abstract
Objective:Exploration of the application of a deep learning reconstruction algorithm(DLIR)and adaptive statistical iterative reconstruction(ASIR-V)based on the combination of low radiation dose and low iodine contrast agent in liver CT enhancement.Methods:A total of 82 patients who underwent abdominal enhanced CT were prospectively selected and randomly separated into groups A and B.Group A(control group)received a conventional dose(tube voltage 120kVp;iodine contrast 85mL)and inferior portal image reconstruction was applied using 30%,50%,and 70%ASIR-V(AV30,AV50,AV70).In Group B(experimental group),image reconstruction was based on medium-and high-intensity deep learning(DLIR-M,DLIR-H).Standard deviation(SD),signal to noise ratio(SNR),contrast to noise ratio(CNR),lesion to liver ratio(LLR),figure of merit(FOM),effective dose(ED),and iodine intake were calculated.Subjective image quality results were obtained for different reconstruction methods at different doses.No significant differences in gender,age,and BMI between groups A and B were found.For 38.40%effective dose and 23.53%reduction in the contrast agent dosage,no significant SD differences were found between DLIR-M and AV50,DLIR-H and liver parenchyma and AV70.Only DLIR-M and AV50 in the portal SNR were not statistically significant.No significant differences were found between DLIR-M and AV70 in liver parenchyma and portal CNR.Concerning LLR and FOM,no significant differences were found between DLIR-M and AV70.For various subjective image quality assessments,DLIR at double low doses outperformed AVIR-V,especially DLIR-H.Conclusions:DLIR can improve image quality and the ability to detect liver low contrast lesions at"double low"(low radiation dose low contrast)compared to ASIR-V.关键词
深度学习重建/双低剂量/上腹部增强CT/肝脏Key words
deep learning/double low dose/upper abdominal enhanced CT/liver分类
信息技术与安全科学
