"Imaging the local nonlinear viscoelastic properties of soft tissues: initial validation and expected benefits"
January 5, 2022
Congratulations to Professor Doyley and former PhD candidate Dr. Rifat Ahmed on the publication of the journal article titled "Imaging the local nonlinear viscoelastic properties of soft tissues: initial validation and expected benefits." UR Department of Biomedical engineering co-authors include Professor Stephen McAleavey, Soumya Goswami, Fan Feng, and Siladitya Khan. This article appears in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. The abstract appears below and more information can be found here.
Abstract: Imaging tissue mechanical properties has shown promise in noninvasive assessment of numerous pathologies. Researchers have successfully measured many linear tissue mechanical properties in laboratory and clinical settings. Currently, multiple complex mechanical effects such as frequency-dependence, anisotropy, and nonlinearity are being investigated separately. However, a concurrent assessment of these complex effects may enable more complete characterization of tissue biomechanics and offer improved diagnostic sensitivity. In this work, we report for the first time a method to map the frequency-dependent nonlinear parameters of soft tissues on a local scale. We recently developed a nonlinear elastography model that combines strain measurements from arbitrary tissue compression with radiation-force-based broadband shear wave speed (WS) measurements. Here, we extended this model to incorporate local measurements of frequency-dependent shear modulus. This combined approach provides a local frequency-dependent nonlinear parameter that can be obtained with arbitrary, clinically realizable tissue compression. Initial assessments using simulations and phantoms validate the accuracy of this approach. We also observed improved contrast in nonlinearity parameter at higher frequencies. Results from ex-vivo liver experiments show 32, 25, 34, and 38 dB higher contrast in elastograms than traditional linear elasticity, elastic nonlinearity, viscosity, and strain imaging methods, respectively. A lesion, artificially created by injection of glutaraldehyde into a liver specimen, showed a 59% increase in the frequency-dependent nonlinear parameter and a 17% increase in contrast ratio.