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In recent years, photoacoustic imaging technology has developed rapidly and has become one of the most important technologies in the field of biomedical imaging. Photoacoustic imaging combines the characteristics of high contrast of optical imaging and strong penetrating power of acoustic imaging. It can obtain tissue imaging with high resolution and can also meet the requirements of quantitative analysis of changes in tissue function and physiological parameters at the same time. So, photoacoustic imaging plays an important role in disease prevention and cancer diagnosis. The traditional information acquisition of photoacoustic imaging is based on Nyquist sampling law (the sampling frequency must be greater than twice the highest signal frequency). This method will waste a lot of sampling resources in photoacoustic imaging with a large amount of data and put forward higher requirements for equipment. In order to break through the limitation of Nyquist sampling law, compressed sensing theory is used to compress and sample the signal. Then the original photoacoustic image is reconstructed by sparse key data. In this paper, Compressive Sampling Matching Pursuit (CoSaMP) is used as the reconstruction algorithm. And the compressed sensing photoacoustic imaging platform is built by K-Wave toolbox (photoacoustic imaging platform tool) of MATLAB simulation software together with the reconstruction algorithm to reconstruct the sparse photoacoustic signals observed. The qualitative and quantitative analysis is carried out on the reconstructed images. Results shows that the reconstruction effect meets the requirements, which verifies the superiority of compressed sensing theory and the reliability and advancement of compressed sensing photoacoustic imaging platform.
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