Depth Resolved Phase Velocity Estimation in Layered Tissue Based On Efficient Additive Attention Network With Surface Acoustic Wave - Optical Coherence Elastography

Le résumé fourni par la source

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 used by analyzing 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 for segmenting depth-resolved bi-layers in OCE, offering significant potential for the clinical identification of sub-surface lesions and abnormalities.

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