De‐noising of photoacoustic sensing and imaging based on combined empirical mode decomposition and independent component analysis

다국어 초록 (Multilingual Abstract)

Photoacoustic (PA) imaging breaks the diffusion limit of conventional optical imaging by listening to the PA wave. As a new kind of functional imaging method, it has experienced tremendous growth in research community with wide range of applications. However, it is still an open and fundamental 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. Conventional approach to enhance the SNR of PA signal in these noisy scenarios is data averaging, which is quite time‐consuming. To improve the signal fidelity and imaging speed, an algorithm of using empirical mode decomposition and independent component analysis de‐noising methods in PA imaging is proposed. The simulation and in vivo experimental results show obvious SNR enhancement of the PA signal and image contrast. The proposed method provides the potential to develop real‐time low‐cost PA imaging system with low‐power laser source.
MAP image of the mice back blood vessel and de‐nosing images in vivo: A, Original MAP image; B, Band‐pass filter de‐noising image; C, EMD de‐noising method image; D, our EMD‐ICA de‐noising method image. EMD, empirical mode decomposition; ICA, independent component analysis.