Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging

Zhuhuang Zhou; Dar-In Tai; Yung-Liang Wan; Jeng-Hwei Tseng; Yi-Ru Lin; Shuicai Wu; Kuen-Cheh Yang; Yin-Yin Liao; Chih-Kuang Yeh; Po-Hsiang Tsui

doi:10.1016/j.ultrasmedbio.2018.03.002

Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging

Ultrasound Med Biol. 2018 Jul;44(7):1327-1340. doi: 10.1016/j.ultrasmedbio.2018.03.002. Epub 2018 Apr 2.

Authors

Zhuhuang Zhou¹, Dar-In Tai², Yung-Liang Wan³, Jeng-Hwei Tseng⁴, Yi-Ru Lin⁵, Shuicai Wu⁶, Kuen-Cheh Yang⁷, Yin-Yin Liao⁸, Chih-Kuang Yeh⁹, Po-Hsiang Tsui¹⁰

Affiliations

¹ College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China; Faculty of Information Technology, Beijing University of Technology, Beijing, China.
² Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan, Taiwan.
³ Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
⁴ Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
⁵ Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
⁶ College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
⁷ Department of Family Medicine, National Taiwan University Hospital, Beihu Branch, Taipei, Taiwan.
⁸ Department of Biomedical Engineering, Hungkuang University, Taichung, Taiwan.
⁹ Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.
¹⁰ Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan. Electronic address: tsuiph@mail.cgu.edu.tw.

PMID: 29622501
DOI: 10.1016/j.ultrasmedbio.2018.03.002

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.

Keywords: Entropy imaging; Fatty liver; Hepatic steatosis; Ultrasound.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Aged
Entropy
Female
Humans
Image Processing, Computer-Assisted / methods*
Liver / diagnostic imaging
Male
Middle Aged
Non-alcoholic Fatty Liver Disease / diagnostic imaging*
Prospective Studies
Taiwan
Ultrasonography / methods*
Young Adult