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Photoacoustic (PA) tomography is an imaging technology that reconstructs the distribution of light absorption in tissue by photoacoustic signals. In recent years, PA tomography has been widely used in anatomical, functional 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 low signal-to-noise ratio (SNR) of photoacoustic signal, especially for deep tissue imaging. Conventional approach to enhance the SNR of photoacoustic signal is data averaging, which is quite time-consuming. In the absence of signal fidelity and imaging speed, an algorithm of using empirical mode decomposition (EMD) and independent component analysis (ICA) de-noising in photoacoustic tomography is proposed. Firstly, the photoacoustic signal is decomposed into a series of intrinsic mode functions (IMFs) with EMD. Each IMF is equivalent to an independent signal. Then, some IMFs are selected to construct the virtual noise channel according to the correlation between IMF and original photoacoustic signal. Finally, the original photoacoustic signal and the virtual noise channel are regarded as the input data for ICA. ICA extracts useful photoacoustic signals from artificially constructed multidimensional data. The de-noised results are compared with that the wavelet de-noising method and bandpass filtering method. The enhancement of the SNR of the photoacoustic signal and the contrast of the reconstructed image have been well demonstrated. The proposed method provides the potential to develop real-time low-cost PA tomography system with low-power laser source and poor PA signal’s SNR.
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