Cao Qiuting, Yan Cheng, Han Xinjun, Wang Yu, Zhao Liqin
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China, 100050.
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 100070.
Acad Radiol. 2022 Jun;29(6):e91-e97. doi: 10.1016/j.acra.2021.07.027. Epub 2021 Oct 13.
To evaluate the material decomposition (MD) techniques in rapid kVp switching dual-energy CT (rsDECT) for quantifying liver fat content in rats with nonalcoholic fatty liver.
Fifty male Sprague-Dawley (SD) rats were divided into study group (n=37) and control group (n = 13) and underwent rsDECT examination at different intervals. All the data analysis was performed using AW4.7 workstation. The fat contents under the traditional fat(water), fat(blood), and fat(muscle) material decomposition (MD) images and the fat volume fraction (FVF) from the liver fat maps generated using multi-material decomposition (MMD) technique were measured. The pathological grades (grade 0, 1, 2 and 3) of fatty liver were determined after euthanasia. The measurement differences among different grades and the correlation of measurements with different grades was analyzed using ANOVA and Spearman correlation, respectively. A receiver operating characteristics (ROC) curve was used to analyze the diagnostic efficacies of fat contents and FVF.
There were statistically significant differences in FVF and fat contents under fat(water), fat(blood), fat(muscle) based MD images among different grades. These values correlated well with the pathological grades (R-value: 0.90, 0.75, 0.79, 0.80, all p <0.001), with FVF having the highest correlation. The area-under-the-curve in ROC of using FVF was the highest, with the cut-off value of 0.92 for sensitivity of 89.2% and specificity of 100%.
The rsDECT MD techniques could quantitatively evaluate the fat content of fatty liver in rat, with the FVF from MMD having the highest correlation with pathological grades.
评估快速千伏切换双能CT(rsDECT)中的物质分解(MD)技术,以量化非酒精性脂肪肝大鼠的肝脏脂肪含量。
将50只雄性Sprague-Dawley(SD)大鼠分为研究组(n = 37)和对照组(n = 13),并在不同时间间隔进行rsDECT检查。所有数据分析均使用AW4.7工作站进行。测量传统脂肪(水)、脂肪(血液)和脂肪(肌肉)物质分解(MD)图像下的脂肪含量,以及使用多物质分解(MMD)技术生成的肝脏脂肪图中的脂肪体积分数(FVF)。安乐死后确定脂肪肝的病理分级(0级、1级、2级和3级)。分别使用方差分析和Spearman相关性分析不同分级之间的测量差异以及测量值与不同分级的相关性。采用受试者工作特征(ROC)曲线分析脂肪含量和FVF的诊断效能。
不同分级之间,基于脂肪(水)、脂肪(血液)、脂肪(肌肉)的MD图像下的FVF和脂肪含量存在统计学显著差异。这些值与病理分级具有良好的相关性(R值:0.90、0.75、0.79、0.80,均p<0.001),其中FVF的相关性最高。使用FVF的ROC曲线下面积最高,截断值为0.92,灵敏度为89.2%,特异性为100%。
rsDECT MD技术可定量评估大鼠脂肪肝的脂肪含量,MMD的FVF与病理分级的相关性最高。