Adaptive Superlet-Based Shear Wave Speed Estimation for Crawling Wave Sonoelastography

Crawling Wave Sonoelastography (CWS) is a quantitative elastography approach that aims to assess tissue stiffness through the Shear Wave Speed (SWS) calculation. CWS is based on the generation of an interference pattern by using external mechanical vibration sources and tracked for particle movement estimation. Further processing enables the SWS map computation by a set of SWS estimators. Current algorithms have incorporated algorithms such as the Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT), which have proven effective in enhancing accuracy, reducing variability, and minimizing artifacts. In this paper, a novel time-frequency estimator based on the Adaptive Superlets (ASLT) is introduced. The experiments were conducted by applying the algorithm to previous datasets, which included both homogeneous and heterogeneous phantoms across various frequency ranges. The performance of the proposed estimator was evaluated in terms of mean, standard deviation, coefficient of variation (CV), and contrast-to-noise ratio (CNR). In addition, a comparison with STFT and CWT is performed. The results show that the ASLT estimator showed a superior performance in terms of lower CV, and a higher CNR against previously reported estimators.