Prevalence of liver disease is continuously increasing and nonalcoholic fatty liver disease (NAFLD) is the most common etiology. We present an approach to detect the progression of liver steatosis based on quantitative ultrasound (QUS) imaging. This study was performed on a group of 55 rats that were subjected to a control or methionine and choline deficient (MCD) diet known to induce NAFLD. Ultrasound (US) measurements were performed at 2 and 6 weeks. Thereafter, animals were humanely euthanized and livers excised for histological analysis. Relative backscatter and attenuation coefficients were simultaneously estimated from the US data and envelope signal-to-noise ratio was calculated to train a regression model for: (1) fat fraction percentage estimation and (2) performing classification according to Brunt’s criteria in grades (0 <5%; 1, 5–33%; 2, >33–66%; 3, >66%) of liver steatosis. The trained regression model achieved an\documentclass[12pt]{minimal}
				\usepackage{amsmath}
				\usepackage{wasysym} 
				\usepackage{amsfonts} 
				\usepackage{amssymb} 
				\usepackage{amsbsy}
				\usepackage{mathrsfs}
				\usepackage{upgreek}
				\setlength{\oddsidemargin}{-69pt}
				\begin{document}$$R^2$$\end{document}R2of 0.97 (p-value < 0.01) and a RMSE of 3.64. Moreover, the classification task reached an accuracy of 94.55%. Our results suggest thatin vivoQUS is a promising noninvasive imaging modality for the early assessment of NAFLD.