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Abstract Motivated by elastography that utilizes tissue mechanical properties as biomarkers for liver disease, with the eventual objective of quantitatively linking histopathology and bulk mechanical properties, we develop a micromechanical modeling approach to capture the effects of fat and collagen deposition in the liver. Specifically, we utilize computational homogenization to convert the microstructural changes in hepatic lobule to the effective viscoelastic modulus of the liver tissue, i.e., predict the bulk material properties by analyzing the deformation of repeating unit cell. The lipid and collagen deposition is simulated with the help of ad hoc algorithms informed by histological observations. Collagen deposition is directly included in the computational model, while composite material theory is used to convert fat content to the microscopic mechanical properties, which in turn is included in the computational model. The results illustrate the model’s ability to capture the effect of both fat and collagen deposition on the viscoelastic moduli and represents a step towards linking histopathological changes in the liver to its bulk mechanical properties, which can eventually provide insights for accurate diagnosis with elastography. 

Abstract Objective. Motivated by the diagnostic value of tissue viscosity beyond elasticity, the goal of this work is to develop robust methodologies based on shear wave elastography (SWE) to reconstruct combined elasticity and viscosity maps of soft tissues out of the measurement plane.Approach.Building on recent advancements in full-waveform inversion in reconstructing elasticity maps beyond the measurement plane, we propose to reconstruct a complete viscoelasticity map by novel combination of three ideas: (a) multiresolution imaging, where lower frequency content is used to reconstruct low resolution map, which is then utilized as a starting point for higher resolution reconstruction by including higher frequency content; (b) acquiring SWE data on multiple planes from multiple pushes, one at a time, and then simultaneously using all the data to invert for a single viscoelasticity map; (c) sequential reconstruction where combined viscoelasticity reconstruction is followed by fixing the elasticity map (and thus kinematics), and repeating the reconstruction but just for the viscosity map.Main results.We examine the proposed methodology using synthetic SWE data to reconstruct the viscoelastic properties of both homogeneous and heterogeneous tumor-like inclusions with shear modulus ranging from 3 to 20 kPa, and viscosity ranging from 1 to 3 Pa·s. Final validation is performedin silico, where the annular inclusion is reconstructed using noisy data with varying signal-to-noise ratios (SNR) of 30, 20 and 10 dB. While elasticity images are reasonably reconstructed even for poor SNR of 10 dB, viscosity imaging seem to require better SNR.Significance.This work, analogous to reconstructing 3D images from 2D measurements, offers a feasibility study for achieving 3D viscoelasticity reconstructions using conventional ultrasound scanners, potentially leading to biomarkers with greater specificity compared to currently available 2D elasticity images.