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Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization
Ultrasonics ( IF 4.2 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.ultras.2020.106308
Zhuhuang Zhou , Anna Gao , Weiwei Wu , Dar-In Tai , Jeng-Hwei Tseng , Shuicai Wu , Po-Hsiang Tsui

The homodyned K (HK) distribution allows a general description of ultrasound backscatter envelope statistics with specific physical meanings. In this study, we proposed a new artificial neural network (ANN) based parameter estimation method of the HK distribution. The proposed ANN estimator took advantages of ANNs in learning and function approximation and inherited the strengths of conventional estimators through extracting five feature parameters from backscatter envelope signals as the input of the ANN: the signal-to-noise ratio (SNR), skewness, kurtosis, as well as X- and U-statistics. Computer simulations and clinical data of hepatic steatosis were used for validations of the proposed ANN estimator. The ANN estimator was compared with the RSK (the level-curve method that uses SNR, skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on X- and U-statistics) estimators. Computer simulation results showed that the relative bias was best for the XU estimator, whilst the normalized standard deviation was overall best for the ANN estimator. The ANN estimator was almost one order of magnitude faster than the RSK and XU estimators. The ANN estimator also yielded comparable diagnostic performance to state-of-the-art HK estimators in the assessment of hepatic steatosis. The proposed ANN estimator has great potential in ultrasound tissue characterization based on the HK distribution.

中文翻译:

基于人工神经网络的零差 K 分布参数估计用于超声组织表征

零差 K (HK) 分布允许对具有特定物理意义的超声反向散射包络统计进行一般描述。在这项研究中,我们提出了一种新的基于人工神经网络 (ANN) 的 HK 分布参数估计方法。所提出的人工神经网络估计器利用人工神经网络在学习和函数逼近方面的优势,通过从反向散射包络信号中提取五个特征参数作为人工神经网络的输入,继承了传统估计器的优点:信噪比(SNR)、偏度、峰度,以及 X 和 U 统计量。肝脏脂肪变性的计算机模拟和临床数据用于验证所提出的人工神经网络估计器。将 ANN 估计量与 RSK(使用 SNR、偏度、和峰度基于包络的分数矩)和 XU(基于 X 和 U 统计的估计方法)估计量。计算机模拟结果表明,相对偏差对于 XU 估计器来说是最好的,而归一化标准偏差对于 ANN 估计器来说总体上是最好的。ANN 估计器几乎比 RSK 和 XU 估计器快一个数量级。ANN 估计器在评估肝脂肪变性方面也产生了与最先进的 HK 估计器相当的诊断性能。所提出的 ANN 估计器在基于 HK 分布的超声组织表征方面具有巨大潜力。而归一化的标准偏差对于 ANN 估计器来说总体上是最好的。ANN 估计器几乎比 RSK 和 XU 估计器快一个数量级。ANN 估计器在评估肝脂肪变性方面也产生了与最先进的 HK 估计器相当的诊断性能。所提出的 ANN 估计器在基于 HK 分布的超声组织表征方面具有巨大潜力。而归一化的标准偏差对于 ANN 估计器来说总体上是最好的。ANN 估计器几乎比 RSK 和 XU 估计器快一个数量级。ANN 估计器在评估肝脂肪变性方面也产生了与最先进的 HK 估计器相当的诊断性能。所提出的 ANN 估计器在基于 HK 分布的超声组织表征方面具有巨大潜力。
更新日期:2021-03-01
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