Performance Evaluations on Using Entropy of Ultrasound Log-Compressed Envelope Images for Hepatic Steatosis Assessment: An In Vivo Animal Study

Abstract

Ultrasound B-mode imaging based on log-compressed envelope data has been widely applied to examine hepatic steatosis. Modeling raw backscattered signals returned from the liver parenchyma by using statistical distributions can provide additional information to assist in hepatic steatosis diagnosis. Since raw data are not always available in modern ultrasound systems, information entropy, which is a widely known nonmodel-based approach, may allow ultrasound backscattering analysis using B-scan for assessing hepatic steatosis. In this study, we explored the feasibility of using ultrasound entropy imaging constructed using log-compressed backscattered envelopes for assessing hepatic steatosis. Different stages of hepatic steatosis were induced in male Wistar rats fed with a methionine-and choline-deficient diet for 0 (i.e., normal control) and 1, 1.5, and 2 weeks (n = 48; 12 rats in each group). In vivo scanning of rat livers was performed using a commercial ultrasound machine (Model 3000, Terason, Burlington, MA, USA) equipped with a 7-MHz linear array transducer (Model 10L5, Terason) for ultrasound B-mode and entropy imaging based on uncompressed (H E image) and log-compressed envelopes (H B image), which were subsequently compared with histopathological examinations. Receiver operating characteristic (ROC) curve analysis and areas under the ROC curves (AUC) were used to assess diagnostic performance levels. The results showed that ultrasound entropy imaging can be used to assess hepatic steatosis. The AUCs obtained from H E imaging for diagnosing different steatosis stages were 0.93 (≥mild), 0.89 (≥moderate), and 0.89 (≥severe), respectively. H B imaging produced AUCs ranging from 0.74 (≥mild) to 0.84 (≥severe) as long as a higher number of bins was used to reconstruct the signal histogram for estimating entropy. The results indicated that entropy use enables ultrasound parametric imaging based on log-compressed envelope signals with great potential for diagnosing hepatic steatosis.

Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome

“…Therefore, when viewing entropy imaging as a new approach for NAFLD diagnosis, it is necessary to not only perform pathological validations but also to explore the metabolic meanings of entropy. Studies have confirmed that the value of ultrasonic entropy is closely related to the pathological changes of hepatic steatosis [ 32 , 33 ]. However, the present study expands our understanding and domain knowledge of ultrasound entropy imaging; we demonstrated that ultrasound entropy imaging can describe the risk of MetS for those with NAFLD and is superior to the conventional ASQ technique.…”

“…This explains why the FD ratio ASQ parameter monotonically decreases with an increase in the degree of hepatic steatosis. Concurrently, the effect of constructive wave interference leads to increases in signal uncertainty and complexity, making the entropy value [ 31 , 32 ].…”

“…By contrast, ultrasound entropy imaging does not require additional signal rejection criteria, and thus it is less influenced by the effects of computational settings and parameter tuning. In addition, the advantages of information entropy lie in entropy estimation no longer being limited by the statistical properties of signals [ 30 , 32 ], implying that entropy is a data-adaptive parameter for ultrasound tissue characterization. A relatively simple but more adaptive computational scheme enables ultrasound entropy imaging to robustly and stably perform NAFLD evaluations, as supported by histopathological validations of both the animal model [ 32 ] and clinical trials [ 31 , 33 ].…”

“…Because the acquired ultrasound backscattered signals digitalized by the imaging system belong to discrete signals, the Shannon entropy of a discrete random variable Y with possible values { y 1 , y 2 , …., y n } (i.e., the envelope data points included within the sliding window) was calculated using the following discrete form: where w (∙) represents the function of probability distribution. In this study, the statistical histogram of the data (bins = 200) was used as an alternative w (∙) for estimation [ 31 , 32 ]. To compare the results of entropy with those of ASQ, ultrasound entropy imaging was constructed using the same WSL (6.9 mm) and WOR (50%).…”

“…In particular, one report demonstrated that information entropy can describe ultrasound backscattered statistics without considering the statistical properties of ultrasound data [ 30 ]. Recent studies have further indicated that entropy parametric imaging enables visualization and characterization of hepatic steatosis, thereby making it possible to implement non-model-based structure quantification of NAFLD [ 31 , 32 , 33 ].…”