Ultrasound Detection of Liver Fibrosis in Individuals with Hepatic Steatosis Using the Homodyned K Distribution

Tsai, Yu-Wei; Zhou, Zhuhuang; Gong, Cihun-Siyong Alex; Tai, Dar‐In; Cristea, Anca; Lin, Yu‐Ching; Tang, Ya-Chun; Tsui, Po-Hsiang

doi:10.1016/j.ultrasmedbio.2020.09.021

Cited by 19 publications

(15 citation statements)

References 47 publications

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“…The Rayleigh distribution generally describes the envelope of the backscattered ultrasound signal, which corresponds to the distribution of the envelope in the case of a high density of random scatterers without a coherent signal component [ 103 104 ]. However, because the distribution of the scattered ultrasound signals within the actual tissue does not always follow the Rayleigh distribution, various statistical models have been proposed [ 103 104 105 106 107 ]. Acoustic structure quantification (ASQ) and the Nakagami distribution have been the most widely studied for tissue characteristics.…”

Section: Qus Techniquesmentioning

confidence: 99%

Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques

Park

Lee

et al. 2022

Korean J Radiol

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Nonalcoholic fatty liver disease, characterized by excessive accumulation of fat in the liver, is the most common chronic liver disease worldwide. The current standard for the detection of hepatic steatosis is liver biopsy; however, it is limited by invasiveness and sampling errors. Accordingly, MR spectroscopy and proton density fat fraction obtained with MRI have been accepted as non-invasive modalities for quantifying hepatic steatosis. Recently, various quantitative ultrasonography techniques have been developed and validated for the quantification of hepatic steatosis. These techniques measure various acoustic parameters, including attenuation coefficient, backscatter coefficient and speckle statistics, speed of sound, and shear wave elastography metrics. In this article, we introduce several representative quantitative ultrasonography techniques and their diagnostic value for the detection of hepatic steatosis.

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Section: Qus Techniquesmentioning

confidence: 99%

Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques

Park

Lee

et al. 2022

Korean J Radiol

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“…However, the effect of liver fibrosis on the time-series patterns of ultrasound signals differs from that of hepatic steatosis. A fat-infiltrated liver with coexisting liver fibrosis could be treated as an inhomogeneous tissue with various scattering cross-sections of the scatterers [32] . Thus, such an inhomogeneity of tissue tends to result in destructive wave interference, significant variations in amplitude, and narrow PDFs for the time-domain signals [33] ( Fig.…”

Section: Discussionmentioning

confidence: 99%

“…For example, a tissue with few scatterers or a homogeneous medium with strong scatterers causes variation in the size of the scattering cross-section, resulting in the same type of backscattered statistics [13] . In addition, because fibrous structures in the liver are acoustically weaker scatterers than are fatty droplets, the formation of backscattered signals tends to be dominated by fatty droplets [32] . For these reasons, changes in the PDF of the backscattered signals caused by liver fibrosis may not be apparent, causing Shannon entropy and other PDF-based entropies to exhibit information-mixing problems that inhibit the detection of liver fibrosis.…”

Section: Discussionmentioning

confidence: 99%

“…Second, most patients with liver fibrosis also had various grades of hepatic steatosis. Although ultrasound parametric imaging based on sample entropy successfully characterized liver fibrosis in the patients with moderate-to-severe hepatic steatosis, using ultrasound backscattering analysis to detect liver fibrosis in livers without hepatic steatosis or with mild hepatic steatosis is substantially more challenging [32] . Using appropriate surrounding tissues and the corresponding sonographic features as references for standardization of entropy analysis and decomposition of features may be necessary to be further investigated.…”

Section: Discussionmentioning

confidence: 99%

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Ultrasound Sample Entropy Imaging: A New Approach for Evaluating Hepatic Steatosis and Fibrosis

Chan

Zhou

Fang

et al. 2021

IEEE J. Transl. Eng. Health Med.

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“…Clinical ultrasound backscattered RF data of liver fibrosis 37 were included ( n = 237). This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital, Taiwan to reuse the clinical data for the purpose of data analysis.…”

Section: Methodsmentioning

confidence: 99%

Liver Fibrosis Assessment Using Radiomics of Ultrasound Homodyned-K imaging Based on the Artificial Neural Network Estimator

et al. 2022

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The homodyned-K distribution is an important ultrasound backscatter envelope statistics model of physical meaning, and the parametric imaging of the model parameters has been explored for quantitative ultrasound tissue characterization. In this paper, we proposed a new method for liver fibrosis characterization by using radiomics of ultrasound backscatter homodyned-K imaging based on an improved artificial neural network (iANN) estimator. The iANN estimator was used to estimate the ultrasound homodyned-K distribution parameters k and α from the backscattered radiofrequency (RF) signals of clinical liver fibrosis ( n = 237), collected with a 3-MHz convex array transducer. The RF data were divided into two groups: Group I corresponded to liver fibrosis with no hepatic steatosis ( n = 94), and Group II corresponded to liver fibrosis with mild to severe hepatic steatosis ( n = 143). The estimated homodyned-K parameter values were then used to construct k and α parametric images using the sliding window technique. Radiomics features of k and α parametric images were extracted, and feature selection was conducted. Logistic regression classification models based on the selected radiomics features were built for staging liver fibrosis. Experimental results showed that the proposed method is overall superior to the radiomics method of uncompressed envelope images when assessing liver fibrosis. Regardless of hepatic steatosis, the proposed method achieved the best performance in staging liver fibrosis ≥ F1, ≥ F4, and the area under the receiver operating characteristic curve was 0.88, 0.85 (Group I), and 0.85, 0.86 (Group II), respectively. Radiomics has improved the ability of ultrasound backscatter statistical parametric imaging to assess liver fibrosis, and is expected to become a new quantitative ultrasound method for liver fibrosis characterization.

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Ultrasound Detection of Liver Fibrosis in Individuals with Hepatic Steatosis Using the Homodyned K Distribution

Cited by 19 publications

References 47 publications

Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques

Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques

Ultrasound Sample Entropy Imaging: A New Approach for Evaluating Hepatic Steatosis and Fibrosis

Liver Fibrosis Assessment Using Radiomics of Ultrasound Homodyned-K imaging Based on the Artificial Neural Network Estimator

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