光声层析成像是依据探测到的光声信号来重建组织内光能量吸收分布图像的一种技术。近年来, 该研究领域得到了巨大的发展, 其应用范围广泛, 包括了解剖学、功能学和分子影像学。然而, 其中一个巨大挑战是由于光声效应的光到声的转换效率非常低, 导致光声信号的信噪比很小, 得到的重建光声图像质量也不高。传统的提高光声信号信噪比的方法是数据平均法, 但严重限制了成像速度。在不牺牲信号保真度和成像速度的情况下, 首先, 利用经验模态分解(Empirical mode decomposition, EMD)实现光声信号的自适应分解, 然后, 以条件互信息为准则确定需要降噪的本征模函数(Intrinsic mode function, IMF), 对选定的本征模函数进行降噪处理得到降噪后的光声信号, 最后利用重构算法得到降噪后的光声图像。仿真和实验结果表明, 提出的这种利用经验模式分解和条件互信息结合的降噪算法对比传统的降噪算法更好地实现了光声信号信噪比的提高和重建图像对比度的提高。证明了该降噪算法的有效性, 同时该方法为低功率激光源和低功率放大信号信噪比的实时低成本PA成像系统的研制提供了可能。

Photoacoustic tomography is a technology that reconstructs the distribution of light energy in tissue through detecting photoacoustic signals. In recent years, the field of research has been greatly developed and widely used in anatomy, functional science and molecular imaging. However, one of the great challenges is that the efficiency of light to sound conversion is very low due to photoacoustic effect, resulting in a low signal-to-noise ratio (SNR) of photoacoustic signal, and the quality of reconstructed photoacoustic image is not high. Conventional approach to enhance the SNR of photoacoustic signal is the data averaging method, but severely limits the imaging speed. Without sacrificing signal fidelity and imaging speed, firstly, uses empirical mode decomposition (EMD) to realize adaptive decomposition of photoacoustic signals. Then uses conditional mutual information as criterion to determine intrinsic mode function (IMF) which needs noise reduction, and then de-noises the selected intrinsic mode functions to obtain the de-noising photoacoustic signal. Finally, the de-noised photoacoustic image is obtained by using the reconstruction algorithm. The simulation and experimental results show that the proposed method, which combines empirical mode decomposition with conditional mutual information, can achieve better improvement of signal-to-noise ratio of photoacoustic signals and the contrast of reconstructed images than the traditional methods. The effectiveness of the de-noising algorithm is proved. At the same time, this method provides a possibility of development a real-time low-cost PA imaging system with low power laser source and low power amplification signal SNR.