The goal of the proposed research is to quantify the contrast provided by quantitative ultrasound (QUS) interrogation techniques for classifying liver as normal, fatty, fibrotic or a combination of fatty fibrotic and to grade the degree of fat or fibrosis in the liver. Fatty liver disease is the most common cause of chronic liver disease and is increasingly leading to more severe liver conditions e.g., hepatocarcinoma, cirrhosis, or complete liver failure. It is estimated that nonalcoholic fatty liver disease (NAFLD) alone affects over 30% of Americans and with increasing problems with obesity in the U.S., NAFLD is poised to become an even more serious medical concern. At present, accurate detection and classification of steatosis (fatty liver) represents a significant medical challenge. Furthermore, the ability to differentiate fatty liver from fibrotic liver and the ability to quantify fibrosis i the liver represents an even more significant medical challenge because the fibrotic liver may be more indicative of severe liver conditions. Liver biopsy continues to be the gold standard for diagnosis of diffuse liver disease. However, there are significant limitations associated with liver biopsy and its use as a screening tool. The use of laboratory and conventional radiological tests has also been explored for the detection and quantification of diffuse liver disease. To date these techniques are also limited because these tests are not sensitive or specific enough to distinguish the grade of fatty liver disease, the grade of fibrosis, and/or are unable to distinguih between fatty and fibrotic liver. Therefore, it is of great importance to medicine to develop noninvasive techniques capable of detecting, classifying and grading normal, fatty and fibrotic liver. We have demonstrated in previous studies the ability of QUS to classify disease based on microstructural features. In addition, our preliminary data suggest that QUS can differentiate fatty liver from normal liver and grade steatosis. Based on this evidence, we believe QUS will provide capabilities that can fill an important and missing role in noninvasive detection and quantification of diffuse liver disease. To accomplish this objective, two specific aims are proposed. The first specific aim is to quantify contrast provided by QUS to noninvasively classify and grade both fatty liver and fibrotic liver in a rabbit model.
This aim will be successfully completed by applying advanced QUS interrogation techniques in vivo to normal, fatty, and fibrotic livers produced in rabbit models of diffuse liver disease. Specifically, through a 5 by 5 factorial study design we will explore the ability of QUS to detect and grade normal, fatty, and fibrotic liver alone and the combined effects of fat and fibrosis on liver disease. The second specific aim is to provide statistical analysis of the QUS parameters provided in specific aim 1 for classification and grading of diffuse liver disease.
This specific aim will be met by combining the QUS parameters acquired from the experiments conducted in the first specific aim in aggregate in normal, fatty and fibrotic liver to create a multiparameter classifier using linear discriminant analysis. A polynomial constraints method will be used to quantify the progression of combined diet and fibrotic injury on liver state.

Public Health Relevance

The proposed project will demonstrate that quantitative ultrasound (QUS) techniques can detect and grade normal, fatty, and fibrotic liver disease and characterize combinations of fatty and fibrotic liver, noninvasively. The advancements will be validated for detecting and quantifying liver disease in a 5 by 5 factorial design study with a rabbit model of diffuse liver disease, which is able to model both steatosis and fibrosis. Classification of normal, fatty and fibrotic liver will combine multiple QUS parameters using linear discriminant analysis.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Pai, Vinay Manjunath
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Illinois Urbana-Champaign
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Luchies, Adam C; Oelze, Michael L (2018) Effects of the container on structure function with impedance map analysis of dense scattering media. J Acoust Soc Am 143:2172
Podkowa, Anthony S; Oelze, Michael L; Ketterling, Jeffrey A (2018) High-Frame-Rate Doppler Ultrasound Using a Repeated Transmit Sequence. Appl Sci (Basel) 8: