Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo

Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor SNR. Conventional solution of employing higher-power laser, or doing long-time signal averaging, may raise the system cost, time consumption, and tissue damage. Another strategy is de-noising algorithm design. In this paper, we propose a gradient-based adaptive wavelet de-noising method, which sets the energy gradient mutation point of low-frequency wavelet components as the threshold. We conducted simulation, ex-vivo and in-vivo experiments using acoustic-resolution PAM. The quality of de-noised PA image/signal by our proposed algorithm has improved by at least 30%, in comparison to the traditional signal denoising algorithms, which produces better contrast and clearer details. Moreover, it produces good results when dealing with multi-layer structures. The proposed de-noising method provides potential to improve the SNR of PA signal under single-shot low-power laser illumination for biomedical applications in vivo.