Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals

This study explored the effects of fatty infiltration on the signal uncertainty of ultrasound backscattered echoes from the liver. Standard ultrasound examinations were performed on 107 volunteers. For each participant, raw ultrasound image data of the right lobe of liver were acquired using a clinical scanner equipped with a 3.5-MHz convex transducer. An algorithmic scheme was proposed for ultrasound B-mode and entropy imaging. Fatty liver stage was evaluated using a sonographic scoring system. Entropy values constructed using the ultrasound radiofrequency (RF) and uncompressed envelope signals (denoted by HR and HE, respectively) as a function of fatty liver stage were analyzed using the Pearson correlation coefficient. Data were expressed as the median and interquartile range (IQR). Receiver operating characteristic (ROC) curve analysis with 95% confidence intervals (CIs) was performed to obtain the area under the ROC curve (AUC). The brightness of the entropy image typically increased as the fatty stage varied from mild to severe. The median value of HR monotonically increased from 4.69 (IQR: 4.60–4.79) to 4.90 (IQR: 4.87–4.92) as the severity of fatty liver increased (r = 0.63, p < 0.0001). Concurrently, the median value of HE increased from 4.80 (IQR: 4.69–4.89) to 5.05 (IQR: 5.02–5.07) (r = 0.69, p < 0.0001). In particular, the AUCs obtained using HE (95% CI) were 0.93 (0.87–0.99), 0.88 (0.82–0.94), and 0.76 (0.65–0.87) for fatty stages ≥mild, ≥moderate, and ≥severe, respectively. The sensitivity, specificity, and accuracy were 93.33%, 83.11%, and 86.00%, respectively (≥mild). Fatty infiltration increases the uncertainty of backscattered signals from livers. Ultrasound entropy imaging has potential for the routine examination of fatty liver disease.

[1]  Chung-Chih Lin,et al.  Entropic Imaging of Cataract Lens: An In Vitro Study , 2014, PloS one.

[2]  Shuicai Wu,et al.  A Computer-Aided Diagnosis Scheme For Detection Of Fatty Liver In Vivo Based On Ultrasound Kurtosis Imaging , 2015, Journal of Medical Systems.

[3]  T. Wilkins,et al.  Nonalcoholic fatty liver disease: diagnosis and management. , 2013, American family physician.

[4]  P M Shankar Comments on 'The effect of logarithmic compression on the estimation of the Nakagami parameter for ultrasonic tissue characterization'. , 2006, Physics in medicine and biology.

[5]  Jae Young Lee,et al.  Hepatic Steatosis: Assessment with Acoustic Structure Quantification of US Imaging. , 2016, Radiology.

[6]  Michael S. Hughes,et al.  Analysis of digitized waveforms using Shannon entropy. II. High‐speed algorithms based on Green’s functions , 1994 .

[7]  C. Lam,et al.  Hepatic steatosis in obese Chinese children , 2004, International Journal of Obesity.

[8]  Hsiang-Yang Ma,et al.  Effects of fatty infiltration in human livers on the backscattered statistics of ultrasound imaging , 2015, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[9]  M. Abraham,et al.  Association between diabetes, family history of diabetes, and risk of nonalcoholic steatohepatitis and fibrosis , 2012, Hepatology.

[10]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[11]  Jacek M. Zurada,et al.  An information-theoretic approach to estimating ultrasound backscatter characteristics , 2004, Comput. Biol. Medicine.

[12]  Chih-Chung Huang,et al.  Characterization of lamina propria and vocal muscle in human vocal fold tissue by ultrasound Nakagami imaging. , 2011, Medical physics.

[13]  H. Busse,et al.  Estimating steatosis and fibrosis: Comparison of acoustic structure quantification with established techniques. , 2015, World journal of gastroenterology.

[14]  A. Berzigotti,et al.  Update on ultrasound imaging of liver fibrosis. , 2013, Journal of hepatology.

[15]  G. Michels,et al.  Acoustic structure quantification ultrasound software proves imprecise in assessing liver fibrosis or cirrhosis in parenchymal liver diseases. , 2014, Ultrasound in medicine & biology.

[16]  Rita J. Miller,et al.  Contribution EX VIVO STUDY OF QUANTITATIVE ULTRASOUND PARAMETERS IN FATTY RABBIT LIVERS , 2012 .

[17]  Ming-de Lu,et al.  Assessment of liver fibrosis in chronic hepatitis B using acoustic structure quantification: quantitative morphological ultrasound , 2016, European Radiology.

[18]  G. Marchesini,et al.  Insulin resistance in nonalcoholic fatty liver disease. , 2010, Current pharmaceutical design.

[19]  H. A-Kader,et al.  Nonalcoholic fatty liver disease: a comprehensive review of a growing epidemic. , 2014, World journal of gastroenterology.

[20]  Raj Vuppalanchi,et al.  Presence and significance of microvesicular steatosis in nonalcoholic fatty liver disease. , 2011, Journal of hepatology.

[21]  J A Zagzebski,et al.  Ultrasound backscatter and attenuation in human liver with diffuse disease. , 1999, Ultrasound in medicine & biology.

[22]  Ming-de Lu,et al.  Impact factors and the optimal parameter of acoustic structure quantification in the assessment of liver fibrosis. , 2015, Ultrasound in medicine & biology.

[23]  P. Shankar A general statistical model for ultrasonic backscattering from tissues , 2000 .

[24]  S A Wickline,et al.  Joint entropy of continuously differentiable ultrasonic waveforms. , 2013, The Journal of the Acoustical Society of America.

[25]  Michael S. Hughes,et al.  Analysis of ultrasonic waveforms using Shannon entropy , 1992, IEEE 1992 Ultrasonics Symposium Proceedings.

[26]  Po-Hsiang Tsui,et al.  Ultrasound Detection of Scatterer Concentration by Weighted Entropy , 2015, Entropy.

[27]  E. Brunt,et al.  Role of liver biopsy in nonalcoholic fatty liver disease. , 2014, World journal of gastroenterology.

[28]  Yoshito Itoh,et al.  Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. , 2014, World journal of gastroenterology.

[29]  G. van Kaick,et al.  Computerized ultrasound B‐scan texture analysis of experimental diffuse parenchymal liver disease: Correlation with histopathology and tissue composition , 1991, Journal of clinical ultrasound : JCU.

[30]  S A Wickline,et al.  Properties of an entropy-based signal receiver with an application to ultrasonic molecular imaging. , 2007, The Journal of the Acoustical Society of America.

[31]  Guido Gerken,et al.  The interaction of hepatic lipid and glucose metabolism in liver diseases. , 2012, Journal of hepatology.

[32]  Yan-Wei Lee,et al.  Relationship Between Ultrasound Backscattered Statistics and the Concentration of Fatty Droplets in Livers: An Animal Study , 2013 .

[33]  Anthony Gamst,et al.  Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat Using a New Quantitative Ultrasound Technique. , 2015, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[34]  Raymond W. Yeung,et al.  A First Course in Information Theory , 2002 .

[35]  Ashwani K. Singal,et al.  Diabetes Mellitus Predicts Occurrence of Cirrhosis and Hepatocellular Cancer in Alcoholic Liver and Non-alcoholic Fatty Liver Diseases , 2015, Journal of clinical and translational hepatology.

[36]  Naohisa Kamiyama,et al.  Real-time ultrasound attenuation imaging of diffuse fatty liver disease. , 2013, Ultrasound in medicine & biology.

[37]  F. Dunn,et al.  Ultrasonic Scattering in Biological Tissues , 1992 .

[38]  Yung-Sheng Chen,et al.  Use of nakagami statistics and empirical mode decomposition for ultrasound tissue characterization by a nonfocused transducer. , 2009, Ultrasound in medicine & biology.

[39]  Y. Takikawa,et al.  Non-invasive determination of hepatic steatosis by acoustic structure quantification from ultrasound echo amplitude. , 2012, World journal of gastroenterology.

[40]  F. Schick,et al.  Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance. , 2009, Journal of hepatology.

[41]  Hiroyuki Hachiya,et al.  B-mode ultrasound with algorithm based on statistical analysis of signals: evaluation of liver fibrosis in patients with chronic hepatitis C. , 2009, AJR. American journal of roentgenology.

[42]  Ruey-Feng Chang,et al.  Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors , 2004, Breast Cancer Research and Treatment.

[43]  U. Rajendra Acharya,et al.  Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm , 2015, Knowl. Based Syst..

[44]  L. Rui,et al.  Energy metabolism in the liver. , 2014, Comprehensive Physiology.

[45]  Michael S. Hughes,et al.  Analysis of digitized waveforms using Shannon entropy , 1993 .

[46]  Haim Azhari,et al.  Feasibility study of ultrasonic fatty liver biopsy: texture vs. attenuation and backscatter. , 2004, Ultrasound in medicine & biology.

[47]  J. Kench,et al.  High sensitivity C‐reactive protein values do not reliably predict the severity of histological changes in NAFLD , 2004, Hepatology.

[48]  Scott B Reeder,et al.  Quantification of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy. , 2011, Radiology.