Wavelet de-noising method with adaptive threshold selection for photoacoustic tomography

Abstract

Photoacoustic (PA) tomography enables imaging of optical absorption property in deep scattering tissue by listening to the PA wave. However, it is an open challenge that the conversion efficiency from light to sound based on PA effect is extremely low. The consequence is the poor signal-to-noise ratio (SNR) of PA signal especially in scenarios of low laser power and deep penetration. The conventional way to improve PA signal's SNR is data averaging, which however severely limits the imaging speed. In this paper, we propose a new adaptive wavelet threshold de-noising (aWTD) algorithm, and apply it in photoacoustic tomography to increase the PA signal's SNR without sacrificing the signal fidelity and imaging speed. PA image quality in terms of contrast is also significantly improved. The proposed method provides the potential to develop real-time low-cost PA tomography system with low-power laser source.