Ultrasonic fatty liver imaging

Abstract

Fatty liver disease is a prevalent condition which may result in serious liver complications and is currently lack of an effective and efficient approach for its quantification. In the paper, we propose to directly image the fat content distribution in liver based on ultrasound echo radio-frequency signals. In the proposed method, spectral difference is utilized to represent the small pieces of liver tissues. Then the connection between the data representation and liver tissues is directly established by an elaborately designed learning process in the high-dimensional feature space, which includes comprehensive hyperparameter learning and model learning. Experimental results demonstrate the effectiveness of the proposed method which is able to visualize the fat distribution and has a 0.93 correlation coefficient with the fat-percentage quantification results of doctor's pathological analysis.