Quantitative Assessment of Lung Ultrasound Grayscale Images Based on Shannon Entropy for the Detection of Pulmonary Aeration

Abstract

Lung ultrasound (LUS) is a radiation-free, affordable, and bedside monitoring method that can detect changes in pulmonary aeration before hypoxic damage. However, visual scoring methods of LUS only enable subjective diagnosis. Therefore, quantitative analysis of LUS is necessary for obtaining objective information on pulmonary aeration. Because raw data are not always available in conventional ultrasound systems, Shannon entropy (ShanEn) of information theory without the requirement of raw data is valuable. In this study, we explored the feasibility of ShanEn estimated through grayscale histogram (GSH) analysis of LUS images for the quantification of pulmonary aeration. Different degrees of pulmonary aeration caused by edema was induced in 32 male New Zealand rabbits intravenously injected with 0.1 mL/kg saline (the control group) and 0.025, 0.05, and 0.1 mL/kg oleic acid (mild, moderate, and severe groups, respectively). In vivo grayscale LUS images were acquired using a commercial point-of-care ultrasound system for estimation of GSH and corresponding ShanEn. Both lungs of each rabbit were dissected, weighed, and dried to determine the wet weight-to-dry weight ratio (W/D) through gravimetry. The determination coefficients of linear correlations between ShanEn and W/D increased from 0.0487 to 0.7477 with gain and dynamic range (DR). In contrast to visual scoring methods of pulmonary aeration that use median gain and low DR, ShanEn for quantifying pulmonary aeration requires high gain and DR. The current findings indicate that ShanEn estimated through GSH analysis of LUS images acquired using conventional ultrasonic imaging systems has great potential to provide objective information on pulmonary aeration.