  Ultrasound shear wave elastography (SWE) is a noninvasive way to measure
stiffness of soft tissue for medical diagnosis. In SWE imaging, an acoustic
radiation force induces tissue displacement, which creates shear waves (SWs)
that travel laterally through the medium. Finding the lateral arrival times of
SWs at different tissue locations helps figure out the shear wave speed (SWS),
which is directly linked to the stiffness of the medium. Traditional SWS
estimation techniques, however, are not noise resilient enough handling noise
and reflection artifacts filled data. This paper proposes new techniques to
estimate SWS in both time and frequency domains. These new methods optimize a
loss function that is based on the lateral signal shift parameter between known
locations and is constrained by neighborhood displacement group shift
determined from the time-lateral plane-denoised SW propagation data. The
proposed constrained optimization is formed by coupling losses of local
particles with a Gaussian kernel giving an optimum arrival time for the center
particle by enforcing stiffness homogeneity in a small neighborhood to enable
inherent noise resilience. The denoising scheme involves isolating the
transitioning SW profile in each time-lateral plane, creating a parameterized
mask. Moreover, lateral interpolation is performed to enhance reconstruction
resolution and obtain increased displacement groups to enhance the reliability
of the optimization. The proposed noise robust scheme is tested on a simulation
and three experimental datasets. The performance of the method is compared with
3 ToF and 2 frequency-domain methods. The evaluations show visually and
quantitatively superior and noise-robust reconstructions compared to
state-of-the-art methods. Due to its high contrast and minimal error, the
proposed technique can find its application in tissue health inspection and
cancer diagnosis.
