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Changes in biomechanical properties of biological soft tissues are often associated with physiological dysfunctions. Since biological soft tissues are hydrated, viscoelasticity is likely suitable to represent its solid-like behavior using elasticity and fluid-like behavior using viscosity. Shear wave elastography is a non-invasive imaging technology invented for clinical applications that has shown promise to characterize various tissue viscoelasticity. It is based on measuring and analyzing velocities and attenuations of propagated shear waves. In this review, principles and technical developments of shear wave elastography for viscoelasticity characterization from organ to cellular levels are presented, and different imaging modalities used to track shear wave propagation are described. At a macroscopic scale, techniques for inducing shear waves using an external mechanical vibration, an acoustic radiation pressure or a Lorentz force are reviewed along with imaging approaches proposed to track shear wave propagation, namely ultrasound, magnetic resonance, optical, and photoacoustic means. Then, approaches for theoretical modeling and tracking of shear waves are detailed. Following it, some examples of applications to characterize the viscoelasticity of various organs are given. At a microscopic scale, a novel cellular shear wave elastography method using an external vibration and optical microscopy is illustrated. Finally, current limitations and future directions in shear wave elastography are presented.

Nine mulard ducks that were being raised for foie gras (steatosis) production went through in vivo shear wave ( SW ) elastography imaging of their liver during the force-feeding period to investigate changes in liver tissue characteristics. A total of 4 imaging sessions at an interval of 3 to 4 d were conducted at the farm on each animal. Three ducks were sacrificed at the second, third, and fourth imaging sessions for histopathology analysis of all animals at these time points. Six SW elastography parameters were evaluated: SW speed, SW attenuation, SW dispersion, Young's modulus, viscosity, and shear modulus. Shear waves of different frequencies propagate with different phase velocities. Thus, SW speed and other dependent parameters such as Young's modulus, viscosity, and shear modulus were computed at 2 frequencies: 75 and 202 Hz. Each parameter depicted a statistically significant trend along the force-feeding process ( P -values between 0.001 and 0.0001). The fat fraction of the liver increased over the 12-day period of feeding. All parameters increased monotonically over time at 75 Hz, whereas modal relations were seen at 202 Hz. Shear wave dispersion measured between 75 and 202 Hz depicted a plateau from day 5. Based on this validation, proposed imaging methods are aimed to be used in the future on naturally fed ducks and geese.

Attenuation map or measurements based on local attenuation coefficient slope (ACS) in quantitative ultrasound (QUS) has shown potential for diagnosis of liver steatosis. In liver cancers, tissue abnormalities and tumors detected using ACS are also of interest to provide new image contrast to clinicians. Current phantom-based approaches have the limitation of assuming comparable speed of sound between the reference phantom and insonified tissues. Moreover, these methods present the inconvenience for operators to acquire data on phantoms as well as on patients. The main goal was to alleviate these drawbacks by proposing a methodology for constructing phantom-free regularized (PF-R) local ACS maps and investigate the performance in both homogeneous and heterogeneous media. The proposed method was tested on two tissue mimicking media with different ACS constructed as homogeneous phantoms, side-byside and top-to-bottom phantoms, and inclusion phantoms with different attenuations. Moreover, an in-vivo proof-of-concept was performed on healthy, steatotic and cancerous human liver datasets. Modifications brought to previous works include: a) a linear interpolation of the power spectrum in log-scale; b) the relaxation of the underlying hypothesis on the diffraction factor; c) a generalization to nonhomogeneous local ACS; and d) an adaptive restriction of frequencies to a more reliable range than the usable frequency range. Regularization was formulated as a generalized LASSO, and a variant of the Bayesian Information Criterion (BIC) was applied to estimate the Lagrangian multiplier on the LASSO constraint. In addition, we evaluated the proposed algorithm when applying median filtering before and after regularization. Tests conducted showed that the PF-R yielded robust results in all tested conditions, suggesting potential for additional validation as a diagnosis method.

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