Hsien-Jung Chan1, Zhuhuang Zhou2, Jui Fang3, Dar-In Tai4, Jeng-Hwei Tseng5, Ming-Wei Lai6, Bao-Yu Hsieh1,5, Tadashi Yamaguchi7, Po-Hsiang Tsui1,6,8. 1. Department of Medical Imaging and Radiological SciencesCollege of Medicine, Chang Gung University Taoyuan 333323 Taiwan. 2. Department of Biomedical EngineeringFaculty of Environment and LifeBeijing University of Technology Beijing 100124 China. 3. X-Dimension Center for Medical Research and TranslationChina Medical University Hospital Taichung 40447 Taiwan. 4. Department of Gastroenterology and HepatologyChang Gung Memorial Hospital at Linkou Taoyuan 333423 Taiwan. 5. Department of Medical Imaging and InterventionChang Gung Memorial Hospital at Linkou Taoyuan 333423 Taiwan. 6. Division of Pediatric GastroenterologyDepartment of PediatricsChang Gung Memorial Hospital at Linkou Taoyuan 333423 Taiwan. 7. Center for Frontier Medical EngineeringChiba University Chiba 263-8522 Japan. 8. Institute for Radiological Research, Chang Gung University Taoyuan 333323 Taiwan.
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 [Formula: see text] and tolerance [Formula: see text], for ultrasound parametric imaging of hepatic steatosis and fibrosis. Methods: Liver donors and patients were enrolled, and their hepatic fat fraction (HFF) ([Formula: see text]), steatosis grade ([Formula: see text]), and fibrosis score ([Formula: see text]) were measured to verify the results of sample entropy imaging using sliding-window processing of ultrasound RF data. Results: The sample entropy calculated using [Formula: see text] 4 and [Formula: see text] 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 [Formula: see text]mild, [Formula: see text]moderate, and [Formula: see text]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.
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 [Formula: see text] and tolerance [Formula: see text], for ultrasound parametric imaging of hepatic steatosis and fibrosis. Methods: Liver donors and patients were enrolled, and their hepatic fat fraction (HFF) ([Formula: see text]), steatosis grade ([Formula: see text]), and fibrosis score ([Formula: see text]) were measured to verify the results of sample entropy imaging using sliding-window processing of ultrasound RF data. Results: The sample entropy calculated using [Formula: see text] 4 and [Formula: see text] 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 [Formula: see text]mild, [Formula: see text]moderate, and [Formula: see text]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.