Ultrasound-derived absorption coefficient quantification for operator-independent fatty liver diagnosis

The proposed learning-based ROI-insensitive absorption coefficient (AC) extraction method in ultrasound (US) aims to improve the reliability of fatty liver diagnosis by addressing operator dependency issues. Traditional methods suffer from variability due to the selection of the region of interest (ROI) and inclusion of unwanted frames. The new approach uses feature maps and a multi-frame collaborative transformation to reduce locational and temporal dependencies. Clinical tests (N=48) showed the method's AC values strongly correlated with proton density fat fraction from MRI (R=0.94, p<0.001), improving correlation and variation by 4% and 90% respectively, indicating its potential as a new diagnostic standard.