BACKGROUND An efficient and accurate approach to quantify the steatosis extent of liver is important for clinical practice. For the purpose, we propose a specific designed ultrasound shear wave sequence to estimate ultrasonic and shear wave physical parameters. The utilization of the estimated quantitative parameters is then studied. RESULTS Shear wave attenuation, shear wave absorption, elasticity, dispersion slope and echo attenuation were simultaneously estimated and quantified from the proposed novel shear wave sequence. Then, a regression tree model was utilized to learn the connection between the space represented by all the physical parameters and the liver fat proportion. MR mDIXON quantification was used as the ground truth for liver fat quantification. Our study included a total of 60 patients. Correlation coefficient (CC) with the ground truth were applied to mainly evaluate different methods for which the corresponding values were - 0.25, - 0.26, 0.028, 0.045, 0.46 and 0.83 for shear wave attenuation, shear wave absorption, elasticity, dispersion slope, echo attenuation and the learning-based model, respectively. The original parameters were extremely outperformed by the learning-based model for which the root mean square error for liver steatosis quantification is only 4.5% that is also state-of-the-art for ultrasound application in the related field. CONCLUSIONS Although individual ultrasonic and shear wave parameters were not perfectly adequate for liver steatosis quantification, a promising result can be achieved by the proposed learning-based acoustic model based on them.

中文翻译：

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通过基于新颖剪切波序列的基于学习的声学模型对超声肝脂肪变性进行量化。

背景技术量化肝脏脂肪变性程度的有效且准确的方法对于临床实践很重要。为此，我们提出了一种经过特殊设计的超声剪切波序列，以估算超声和剪切波的物理参数。然后研究估计的定量参数的利用。结果剪切波衰减，剪切波吸收，弹性，色散斜率和回波衰减同时从提出的新型剪切波序列中估计和量化。然后，利用回归树模型来了解所有物理参数所代表的空间与肝脏脂肪比例之间的联系。MR mDIXON定量被用作肝脂肪定量的基础。我们的研究共包括60名患者。应用与地面真实性的相关系数（CC）来主要评估不同的方法，其剪切波衰减，剪切波吸收，弹性，色散斜率，回波的对应值分别为-0.25，-0.26、0.028、0.045、0.46和0.83衰减和基于学习的模型。最初的参数大大优于基于学习的模型，该模型的肝脏脂肪变性量化的均方根误差仅为4.5％，这也是相关领域超声应用的最新技术。结论尽管单独的超声和切变波参数不能完全满足肝脏脂肪变性的量化要求，但基于它们的基于学习的声学模型可以实现令人鼓舞的结果。