title: An analysis of intrinsic variations of low-frequency shear wave speed in a stochastic tissue model: the first application for staging liver fibrosis.
abstract: Shear wave elastography is increasingly being used to non-invasively stage liver fibrosis by measuring shear wave speed (SWS). This study quantitatively investigates intrinsic variations among SWS measurements obtained from heterogeneous media such as fibrotic livers. More specifically, it aims to demonstrate that intrinsic variations in SWS measurements, in general, follow a non-Gaussian distribution and are related to the heterogeneous nature of the medium being measured. Using the principle of maximum entropy (ME), our primary objective is to derive a probability density function (PDF) of the SWS distribution in conjunction with a lossless stochastic tissue model. Our secondary objective is to evaluate the performance of the proposed PDF using Monte Carlo (MC)-simulated shear wave (SW) data against three other commonly used PDFs. Based on statistical evaluation criteria, initial results showed that the derived PDF fits better to MC-simulated SWS data than the other three PDFs. It was also found that SW fronts stabilized after a short (compared with the SW wavelength) travel distance in lossless media. Furthermore, in lossless media, the distance required to stabilize the SW propagation was not correlated to the SW wavelength at the low frequencies investigated (i.e. 50, 100 and 150 Hz). Examination of the MC simulation data suggests that elastic (shear) wave scattering became more pronounced when the volume fraction of hard inclusions increased from 10 to 30%. In conclusion, using the principle of ME, we theoretically demonstrated for the first time that SWS measurements in this model follow a non-Gaussian distribution. Preliminary data indicated that the proposed PDF can quantitatively represent intrinsic variations in SWS measurements simulated using a two-phase random medium model. The advantages of the proposed PDF are its physically meaningful parameters and solid theoretical basis.
authors.affiliation: Department of Biomedical Engineering, Michigan Technological University, Houghton, MI 49931, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Wang
authors.forename: Min
authors.initials: M
authors.lastname: Wang
authors.forename: Jingfeng
authors.initials: J
authors.lastname: Jiang
mesh_terms: D000704:Analysis of Variance; D054459:Elasticity Imaging Techniques / Q000379:methods / Q000592:standards*; D006801:Humans; D008103:Liver Cirrhosis / Q000000981:diagnostic imaging*; D009010:Monte Carlo Method