Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl₄. The ground truth of the liver state was based on lipid liver percents estimated via the Folch assay and hydroxyproline concentration to quantify fibrosis. Rabbits were scanned ultrasonically in vivo using a SonixOne scanner and an L9-4/38 linear array. Liver fat percentage was classified based on the ultrasonic backscattered radiofrequency (RF) signals from the livers using either QUS or a 1-D convolutional neural network (CNN). Use of QUS parameters with linear regression and canonical correlation analysis demonstrated that the QUS parameters could differentiate between livers with lipid levels above or below 5%. However, the QUS parameters were not sensitive to fibrosis. The CNN was implemented by analyzing raw RF ultrasound signals without using separate reference data. The CNN outputs the classification of liver as either above or below a threshold of 5% fat level in the liver. The CNN outperformed the classification utilizing the QUS parameters combined with a support vector machine in differentiating between low and high lipid liver levels (i.e., accuracies of 74% versus 59% on the testing data). Therefore, although the CNN did not provide a physical interpretation of the tissue properties (e.g., attenuation of the medium or scatterer properties) the CNN had much higher accuracy in predicting fatty liver state and did not require an external reference scan.

定量超声 (QUS) 用于对被诱导患有肝病的兔子进行分类，方法是在规定的时间内将它们置于高脂肪饮食和/或定期给它们注射 CCl ₄。肝脏状态的基本事实是基于通过估计的脂质肝脏百分比Folch 测定和羟脯氨酸浓度以量化纤维化。使用 SonixOne 扫描仪和 L9-4/38 线性阵列对兔子进行体内超声扫描。使用 QUS 或一维卷积神经网络 (CNN) 根据来自肝脏的超声反向散射射频 (RF) 信号对肝脏脂肪百分比进行分类。使用具有线性回归和典型相关分析的 QUS 参数表明，QUS 参数可以区分脂质水平高于或低于 5% 的肝脏。然而，QUS 参数对纤维化不敏感。CNN 是通过分析原始射频超声信号而不使用单独的参考数据来实现的。CNN 将肝脏分类输出为高于或低于肝脏中 5% 脂肪水平的阈值。即，测试数据的准确率分别为 74% 和 59%）。因此，尽管 CNN 没有提供组织特性的物理解释（例如，介质或散射特性的衰减），但 CNN 在预测脂肪肝状态方面具有更高的准确度，并且不需要外部参考扫描。