Characterizing random complex biological media by quantifying ultrasound multiple scattering

Introduction: In this in silico, in vitro, and in vivo study, we propose metrics for the characterization of highly scattering media using backscattered acoustic waves in the MHz range, for application to the characterization of biological media. Methods: Multi-element array transducers are used to record the ultrasonic Inter element Response Matrix (IRM) of scattering phantoms and of lung tissue in rodent models of pulmonary fibrosis. The distribution of singular values of the IRM in the frequency domain is then studied to quantify the multiple scattering contribution. Numerical models of scattering media, as well as gelatin-glass bead and polydimethylsiloxane phantoms with different scatterer densities, are used as a first step to demonstrate the proof of concept. Results: The results show that changes in microstructure of a complex random medium affect parameters associated with the distribution of singular values. Two metrics are proposed: E ( X ) , which is the expected value of the singular value distribution, and λ max , the maximum value of the probability density function of the singular value distribution, i.e., the most represented singular value. After validation of the methods in silico and in phantoms, we show that these metrics are relevant to evaluate pulmonary fibrosis in an in vivo rodent study on six control rats and eighteen rats with varying degrees of severity of pulmonary fibrosis. In rats, a moderate correlation was found between the severity of pulmonary fibrosis and metrics E ( X ) and λ max . Discussion: These results suggest that such parameters could be used as metrics to estimate the amount of multiple scattering in highly heterogeneous media, and that these parameters could contribute to the evaluation of structural changes in lung microstructure.