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

Hsien Jung Chan, Zhuhuang Zhou, Jui Fang, Dar In Tai, Jeng Hwei Tseng, Ming Wei Lai, Bao Yu Hsieh, Tadashi Yamaguchi, Po Hsiang Tsui*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

6 Scopus citations

Abstract

Objective: Hepatic steatosis causes nonalcoholic fatty liver disease and may progress to fibrosis. Ultrasound is the first-line approach to examining hepatic steatosis. Fatty droplets in the liver parenchyma alter ultrasound radiofrequency (RF) signal statistical properties. This study proposes using sample entropy, a measure of irregularity in time-series data determined by the dimension $m$ and tolerance $r$ , for ultrasound parametric imaging of hepatic steatosis and fibrosis. Methods: Liver donors and patients were enrolled, and their hepatic fat fraction (HFF) ( $n =72$ ), steatosis grade ( $n =286$ ), and fibrosis score ( $n =65$ ) were measured to verify the results of sample entropy imaging using sliding-window processing of ultrasound RF data. Results: The sample entropy calculated using $m =$ 4 and $r =0.1$ was highly correlated with the HFF when a small window with a side length of one pulse was used. The areas under the receiver operating characteristic curve for detecting hepatic steatosis that was $\ge $ mild, $\ge $ moderate, and $\ge $ severe were 0.86, 0.90, and 0.88, respectively, and the area was 0.87 for detecting liver fibrosis in individuals with significant steatosis. Discussion/Conclusions: Ultrasound sample entropy imaging enables the identification of time-series patterns in RF signals received from the liver. The algorithmic scheme proposed in this study is compatible with general ultrasound pulse-echo systems, allowing clinical fibrosis risk evaluations of individuals with developing hepatic steatosis.

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Fatty liver
  • hepatic steatosis
  • sample entropy

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