摘要
目的探讨深度学习图像重建算法(DLIR)在腹部低剂量CT中提高图像质量和降低伪影方面的价值。方法前瞻性连续纳入2019年10月-2020年6月进行CT尿路造影的患者26例,男14例,女12例,平均年龄(60.35±10.89)岁。所有患者均行常规剂量平扫、门静脉期增强(噪声指数10;体积CT剂量指数:9.61 mGy)和低剂量排泄期扫描(噪声指数23;体积CT剂量指数:2.95 mGy)。排泄期图像采用ASiR-V 50%、低强度DLIR(DLIR-L)、中强度DLIR(DLIR-M)、高强度DLIR(DLIR-H)共4种方式重建,采用重复测量的单因素方差分析和Kruskal-Wallis H检验分别比较4组图像的客观评价[偏度、噪声、信噪比(SNR)及对比噪声比(CNR)]及主观评价内容(图像质量、噪声、伪影),并采用Bonferroni检验进行事后两两比较。结果无论是客观评价还是主观评价方面,DLIR图像的SNR、CNR、整体图像质量评分及噪声均相似或优于ASiR-V 50%,且SNR、CNR和图像质量评分随DLIR权重增加而增加,噪声随着DLIR权重增加而降低。4组图像在失真伪影(P=0.776)和对比剂硬化伪影(P=0.881)主观评分中差异不具有统计学意义。结论与ASiR-V 50%算法相比,DLIR特别是DLIR-M和DLIR-H,可显著提高腹部低剂量CT的图像质量,但在降低对比剂硬化伪影方面应用有限。
Objective To investigate the value of deep learning image reconstruction(DLIR)in improving image quality and reducing beam-hardening artifacts of low-dose abdominal CT.Methods For this study we prospectively enrolled 26 patients(14 males and 12 females,mean age of 60.35±10.89 years old)who underwent CT urography between October 2019 and June 2020.All the patients underwent conventional-dose unenhanced CT and contrast-enhanced CT in the portal venous phase(noise index of 10;volume computed tomographic dose index:9.61 mGy)and low-dose CT in the excretory phase(noise index of 23;volume computed tomographic dose index:2.95 mGy).CT images in the excretory phase were reconstructed using four algorithms:ASiR-V 50%,DLIR-L,DLIR-M,and DLIR-H.Repeated measures ANOVA and Kruskal-Wallis H test were used to compare the quantitative(skewness,noise,SNR,CNR)and qualitative(image quality,noise,beam-hardening artifacts)values among the four image groups.Post hoc comparisons were performed using Bonferroni test.Results In either quantitative or qualitative evaluation,the SNR,CNR,overall image quality score,and noise of DLIR images were similar or better than ASiR-V 50%.In addition,the SNR,CNR,and overall image quality scores increased as the DLIR weight increased,while the noise decreased.There was no statistically significant difference in the distortion artifacts(P=0.776)and contrast-induced beam-hardening artifacts(P=0.881)scores among these groups.Conclusion Compared with the ASiR-V 50%algorithm,DLIR algorithm,especially DLIR-M and DLIR-H,can significantly improve the image quality of low-dose abdominal CT,but has limitations in reducing contrast-induced beam-hardening artifacts.
作者
程燕南
孙精涛
李雅楠
郭银霞
曹乐
杨建
杨健
郭建新
CHENG Yannan;SUN Jingtao;LI Yanan;GUO Yinxia;CAO Le;YANG Jian;YANG Jian;GUO Jianxin(Department of Radiology,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,China;Clinical Research Center,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,China)
出处
《西安交通大学学报（医学版）》
CAS
CSCD
北大核心
2023年第3期466-472,共7页
Journal of Xi’an Jiaotong University（Medical Sciences）
基金
陕西省高校联合项目一般项目(No.2020GXLH-Y-026)
西安交通大学第一附属医院科研发展基金(No.2018MS-27&No.2021ZXY-04)。
