Depth-resolved phase velocity estimation in layered tissue based on an efficient additive attention network with surface acoustic wave - optical coherence elastography.

Optical coherence elastography (OCE) is a non-invasive imaging technique used to quantify tissue stiffness and to assist in the diagnosis and assessment of disease. A major limitation of conventional OCE approaches is that phase velocity estimation requires transformation from the spatial-temporal domain to the frequency-wavenumber domain, a process that is computationally inefficient and may introduce errors due to assumptions regarding tissue properties. We propose a unified framework for depth-resolved phase velocity estimation that combines spectral analysis of complex-valued signals with a deep learning inversion network. The effectiveness of the framework is validated using homogeneous agar phantoms, while layered agar phantoms and in vivo human skin are analyzed by depth-dependent phase velocity gradients. The proposed phase velocity estimation network (PVNet) achieved a mean absolute error (MAE) of 0.123 ± 0.024 m/s in agar models and 0.145 ± 0.114 m/s in human skin, compared with ground truth measurements. This study presents a deep learning approach for segmenting depth-resolved bi-layers in OCE, offering significant potential for the clinical identification of sub-surface lesions and abnormalities.