Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm
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.
冯乃章：Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
沈毅：Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
李建刚：Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
马立勇：Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
伍政华：Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
备注：This work was supported by the National Natural Science Foundation of China (No. 30800240), the Shandong Provincial Key Science-Technology Project (No. 2009GG10001006), the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-Aged Scientists (No. BS2010DX001), and the Weihai City Science & Technology Development Project (No. 2010-3-96).
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