Shear wave speed reconstruction via combining virtual rotation and directional filtering for enhanced tissue characterization

Abstract Objective. This study aims to enhance the reliability of shear wave speed (SWS) reconstruction in ultrasound shear wave imaging (SWI) for improved tissue characterization, particularly in complex media where wave attenuation, diffraction, and refraction pose significant challenges. Approach. We propose a novel method (VirR-DF-xcorr-Fit) combining virtual rotation (VirR, a data-driven rotational analysis of shear wave propagation images) and one-dimensional directional filtering (DF). First, we analyzed SWS bias in conventional cross-correlation-based (xcorr) SWI when waves deviate from the lateral direction. We then derived a theoretical equation relating horizontal SWS ( v X ) to true SWS ( v 0 ) and image rotation angle ( β ). SWS data were processed with VirR and DF, and fitted to this equation to estimate v 0 and reduce SWS bias. Main results. Compared with baseline methods (xcorr, DF-xcorr, fast shear compounding), VirR-DF-xcorr-Fit improved contrast-to-noise ratio and edge accuracy for identifying stiff circular inclusions in both in-silico and in-vitro phantoms, with a slight reduction in edge sharpness. It also achieved more satisfactory edge detection for non-circular (star-like polygonal) inclusion in in-silico phantom, preserving boundary integrity with fewer artifacts. Significance. The proposed method refines SWS reconstruction in SWI through the integration of VirR and DF, enhancing the technique’s ability to characterize biological tissues. This advancement is critical for quantitative stiffness mapping in clinical applications such as liver fibrosis assessment and breast cancer detection.