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Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging.

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Title: Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging.
Authors: Zhou, Zhuhuang1,2, Tai, Dar-In3, Wan, Yung-Liang4,5,6, Tseng, Jeng-Hwei4, Lin, Yi-Ru7, Wu, Shuicai1, Yang, Kuen-Cheh8, Liao, Yin-Yin9, Yeh, Chih-Kuang10, Tsui, Po-Hsiang4,5,6 tsuiph@mail.cgu.edu.tw
Source: Ultrasound in Medicine & Biology. Jul2018, Vol. 44 Issue 7, p1327-1340. 14p.
Subjects: Ultrasonic imaging, Fatty liver, Entropy, Backscattering, Image analysis, Diagnosis, Comparative studies, Digital image processing, Liver, Longitudinal method, Research methodology, Medical cooperation, Physics, Research, Evaluation research
Geographic Terms: Taiwan
Abstract: Nonalcoholic fatty liver disease is a type of hepatic steatosis that is not only associated with critical metabolic risk factors but can also result in advanced liver diseases. Ultrasound parametric imaging, which is based on statistical models, assesses fatty liver changes, using quantitative visualization of hepatic-steatosis-caused variations in the statistical properties of backscattered signals. One constraint with using statistical models in ultrasound imaging is that ultrasound data must conform to the distribution employed. Small-window entropy imaging was recently proposed as a non-model-based parametric imaging technique with physical meanings of backscattered statistics. In this study, we explored the feasibility of using small-window entropy imaging in the assessment of fatty liver disease and evaluated its performance through comparisons with parametric imaging based on the Nakagami distribution model (currently the most frequently used statistical model). Liver donors (n = 53) and patients (n = 142) were recruited to evaluate hepatic fat fractions (HFFs), using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate and severe), using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct B-mode, small-window entropy and Nakagami images to correlate with HFF analyses and fatty liver stages. The diagnostic values of the imaging methods were evaluated using receiver operating characteristic curves. The results demonstrated that the entropy value obtained using small-window entropy imaging correlated well with log10(HFF), with a correlation coefficient r = 0.74, which was higher than those obtained for the B-scan and Nakagami images. Moreover, small-window entropy imaging also resulted in the highest area under the receiver operating characteristic curve (0.80 for stages equal to or more severe than mild; 0.90 for equal to or more severe than moderate; 0.89 for severe), which indicated that non-model-based entropy imaging-using the small-window technique-performs more favorably than other techniques in fatty liver assessment. [ABSTRACT FROM AUTHOR]
Copyright of Ultrasound in Medicine & Biology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging.
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  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Zhuhuang%22">Zhou, Zhuhuang</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Tai%2C+Dar-In%22">Tai, Dar-In</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wan%2C+Yung-Liang%22">Wan, Yung-Liang</searchLink><relatesTo>4,5,6</relatesTo><br /><searchLink fieldCode="AR" term="%22Tseng%2C+Jeng-Hwei%22">Tseng, Jeng-Hwei</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Lin%2C+Yi-Ru%22">Lin, Yi-Ru</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Wu%2C+Shuicai%22">Wu, Shuicai</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Kuen-Cheh%22">Yang, Kuen-Cheh</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Liao%2C+Yin-Yin%22">Liao, Yin-Yin</searchLink><relatesTo>9</relatesTo><br /><searchLink fieldCode="AR" term="%22Yeh%2C+Chih-Kuang%22">Yeh, Chih-Kuang</searchLink><relatesTo>10</relatesTo><br /><searchLink fieldCode="AR" term="%22Tsui%2C+Po-Hsiang%22">Tsui, Po-Hsiang</searchLink><relatesTo>4,5,6</relatesTo><i> tsuiph@mail.cgu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Ultrasound+in+Medicine+%26+Biology%22">Ultrasound in Medicine & Biology</searchLink>. Jul2018, Vol. 44 Issue 7, p1327-1340. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Ultrasonic+imaging%22">Ultrasonic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Fatty+liver%22">Fatty liver</searchLink><br /><searchLink fieldCode="DE" term="%22Entropy%22">Entropy</searchLink><br /><searchLink fieldCode="DE" term="%22Backscattering%22">Backscattering</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Liver%22">Liver</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+cooperation%22">Medical cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+research%22">Evaluation research</searchLink>
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  Data: Nonalcoholic fatty liver disease is a type of hepatic steatosis that is not only associated with critical metabolic risk factors but can also result in advanced liver diseases. Ultrasound parametric imaging, which is based on statistical models, assesses fatty liver changes, using quantitative visualization of hepatic-steatosis-caused variations in the statistical properties of backscattered signals. One constraint with using statistical models in ultrasound imaging is that ultrasound data must conform to the distribution employed. Small-window entropy imaging was recently proposed as a non-model-based parametric imaging technique with physical meanings of backscattered statistics. In this study, we explored the feasibility of using small-window entropy imaging in the assessment of fatty liver disease and evaluated its performance through comparisons with parametric imaging based on the Nakagami distribution model (currently the most frequently used statistical model). Liver donors (n = 53) and patients (n = 142) were recruited to evaluate hepatic fat fractions (HFFs), using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate and severe), using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct B-mode, small-window entropy and Nakagami images to correlate with HFF analyses and fatty liver stages. The diagnostic values of the imaging methods were evaluated using receiver operating characteristic curves. The results demonstrated that the entropy value obtained using small-window entropy imaging correlated well with log10(HFF), with a correlation coefficient r = 0.74, which was higher than those obtained for the B-scan and Nakagami images. Moreover, small-window entropy imaging also resulted in the highest area under the receiver operating characteristic curve (0.80 for stages equal to or more severe than mild; 0.90 for equal to or more severe than moderate; 0.89 for severe), which indicated that non-model-based entropy imaging-using the small-window technique-performs more favorably than other techniques in fatty liver assessment. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Ultrasound in Medicine & Biology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.ultrasmedbio.2018.03.002
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 1327
    Subjects:
      – SubjectFull: Ultrasonic imaging
        Type: general
      – SubjectFull: Fatty liver
        Type: general
      – SubjectFull: Entropy
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      – SubjectFull: Backscattering
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      – SubjectFull: Image analysis
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      – SubjectFull: Diagnosis
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      – SubjectFull: Comparative studies
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      – SubjectFull: Digital image processing
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      – SubjectFull: Liver
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      – SubjectFull: Longitudinal method
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      – SubjectFull: Research methodology
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      – SubjectFull: Medical cooperation
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      – SubjectFull: Evaluation research
        Type: general
      – SubjectFull: Taiwan
        Type: general
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      – TitleFull: Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging.
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              Text: Jul2018
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