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

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.