Improving diagnostic accuracy of ultrasound texture features in detecting and quantifying hepatic steatosis using various beamforming sound speeds.

While ultrasound image texture has been utilized to detect and quantify hepatic steatosis, the texture features extracted using a single (conventionally 1540 m s) beamforming speed of sound (SoS) failed to achieve reliable diagnostic performance. This study aimed to investigate if the texture features extracted using various beamforming SoSs can improve the accuracy of hepatic steatosis detection and quantification.Patients with suspected non-alcoholic fatty liver disease underwent liver biopsy or MRI proton density fat fraction (PDFF) as part of standard of care, were prospectively enrolled. The radio-frequency data from subjects' right and left liver lobes were collected using 6 beamforming SoSs: 1300, 1350, 1400, 1450, 1500 and 1540 m sand analyzed offline. The texture features, i.e. Contrast, Correlation, Energy and Homogeneity from gray-level co-occurrence matrix of normalized envelope were obtained from a region of interest in the liver parenchyma.Forty-three subjects (67.2%) were diagnosed with steatosis while 21 had no steatosis. Homogeneity showed the area under the curve (AUC) of 0.75-0.82 and 0.58-0.81 for left and right lobes, respectively with varying beamforming SoSs. The combined Homogeneity value over 1300-1540 m sfrom left and right lobes showed the AUC of 0.90 and 0.81, respectively. Furthermore, the combined Homogeneity values from left and right lobes over 1300-1540 m simproved the AUC to 0.94. The correlation between texture features and steatosis severity was improved by using the images from various beamforming SoSs. The combined Contrast values over 1300-1540 m sfrom left and right lobes demonstrated the highest correlation (= 0.90) with the MRI PDFF while the combined Homogeneity values over 1300-1540 m sfrom left and right lobes showed the highest correlation with the biopsy grades (= -0.81).The diagnostic accuracy of ultrasound texture features in detecting and quantifying hepatic steatosis was improved by combining its values extracted using various beamforming SoSs.