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Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm
Authors:Mingjian Sun  Naizhang Feng  Yi Shen  Jiangang Li  Liyong Ma  and Zhenghua Wu
Institution:Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Abstract:The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.
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