A Constrained Optimization approach for Ultrasound Shear Wave Speed Estimation with Time-Lateral Plane Cleaning

TL;DR

This paper introduces a constrained optimization method for ultrasound shear wave speed estimation that enhances noise robustness and accuracy by leveraging neighborhood displacement constraints and lateral plane denoising, improving tissue stiffness assessment.

Contribution

The paper presents a novel constrained optimization approach for shear wave speed estimation that integrates neighborhood stiffness homogeneity and denoising, outperforming existing methods in noisy conditions.

Findings

Superior noise resilience compared to state-of-the-art methods.

Enhanced accuracy in shear wave speed estimation.

Effective in both simulated and experimental datasets.

Abstract

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.