Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Ultrasound Med Biol. 2019 Aug;45(8):2049-2062. doi: 10.1016/j.ultrasmedbio.2019.03.021. Epub 2019 May 8.
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and can often lead to fibrosis, cirrhosis, cancer and complete liver failure. Liver biopsy is the current standard of care to quantify hepatic steatosis, but it comes with increased patient risk and only samples a small portion of the liver. Imaging approaches to assess NAFLD include proton density fat fraction estimated via magnetic resonance imaging (MRI) and shear wave elastography. However, MRI is expensive and shear wave elastography is not proven to be sensitive to fat content of the liver (Kramer et al. 2016). On the other hand, ultrasonic attenuation and the backscatter coefficient (BSC) have been observed to be sensitive to levels of fat in the liver (Lin et al. 2015; Paige et al. 2017). In this study, we assessed the use of attenuation and the BSC to quantify hepatic steatosis in vivo in a rabbit model of fatty liver. Rabbits were maintained on a high-fat diet for 0, 1, 2, 3 or 6 wk, with 3 rabbits per diet group (total N = 15). An array transducer (L9-4) with a center frequency of 4.5 MHz connected to a SonixOne scanner was used to gather radio frequency (RF) backscattered data in vivo from rabbits. The RF signals were used to estimate an average attenuation and BSC for each rabbit. Two approaches were used to parameterize the BSC (i.e., the effective scatterer diameter and effective acoustic concentration using a spherical Gaussian model and a model-free approach using a principal component analysis [PCA]). The 2 major components of the PCA from the BSCs, which captured 96% of the variance of the transformed data, were used to generate input features to a support vector machine for classification. Rabbits were separated into two liver fat-level classes, such that approximately half of the rabbits were in the low-lipid class (≤9% lipid liver level) and half of the rabbits in the high-lipid class (>9% lipid liver level). The slope and the midband fit of the attenuation coefficient provided statistically significant differences (p value = 0.00014 and p value = 0.007, using a two-sample t test) between low and high-lipid fat classes. The proposed model-free and model-based parameterization of the BSC and attenuation coefficient parameters yielded classification accuracies of 84.11 %, 82.93 % and 78.91 % for differentiating low-lipid versus high-lipid classes, respectively. The results suggest that attenuation and BSC analysis can differentiate low-fat versus high-fat livers in a rabbit model of fatty liver disease.
非酒精性脂肪性肝病（NAFLD）是慢性肝病最常见的原因，通常可导致纤维化、肝硬化、癌症和肝衰竭。肝活检是目前量化肝脂肪变性的标准方法，但会增加患者的风险，并且仅对肝脏的一小部分进行采样。评估 NAFLD 的影像学方法包括通过磁共振成像（MRI）估计质子密度脂肪分数和剪切波弹性成像。然而，MRI 昂贵，剪切波弹性成像不能证明对肝脏脂肪含量敏感（Kramer 等人，2016 年）。另一方面，超声衰减和反向散射系数（BSC）已被观察到对肝脏中的脂肪水平敏感（Lin 等人，2015 年；Paige 等人，2017 年）。在这项研究中，我们评估了在高脂肪饮食诱导的兔脂肪肝模型中使用衰减和 BSC 来量化肝脂肪变性。兔子接受高脂肪饮食 0、1、2、3 或 6 周，每组 3 只兔子（共 15 只）。使用中心频率为 4.5 MHz 的阵列换能器（L9-4）连接到 SonixOne 扫描仪，从兔子体内收集射频（RF）反向散射数据。RF 信号用于估计每个兔子的平均衰减和 BSC。使用两种方法对 BSC 进行参数化（即使用球形高斯模型的有效散射体直径和有效声浓度和使用主成分分析（PCA）的无模型方法）。BSC 的两个主要成分（捕获转换后数据方差的 96%）用于为支持向量机生成输入特征以进行分类。兔子被分为两个肝脏脂肪水平类别，使得大约一半的兔子处于低脂质水平类别（≤9%的脂质肝脏水平），另一半兔子处于高脂质水平类别（>9%的脂质肝脏水平）。衰减系数的斜率和中带拟合在低脂质和高脂质脂肪类之间提供了统计学上的显著差异（p 值=0.00014 和 p 值=0.007，使用双样本 t 检验）。BSC 和衰减系数参数的拟议无模型和基于模型的参数化分别产生了区分低脂质与高脂质类别的分类准确率 84.11%、82.93%和 78.91%。结果表明，衰减和 BSC 分析可区分兔脂肪性肝病模型中的低脂与高脂肝脏。
