Abstract
The recently emerging technique of sparse reconstruction has received much attention in the field of photoacoustic imaging (PAI). Compressed sensing (CS) has large potential in efficiently reconstructing high-quality PAI images with sparse sampling signal. In this article, we propose a CS-based error-tolerant regularized smooth L0 (ReSL0) algorithm for PAI image reconstruction, which has the same computational advantages as the SL0 algorithm while having a higher degree of immunity to inaccuracy caused by noise. In order to evaluate the performance of the ReSL0 algorithm, we reconstruct the simulated dataset obtained from three phantoms. In addition, a real experimental dataset from agar phantom is also used to verify the effectiveness of the ReSL0 algorithm. Compared to three L0 norm, L1 norm, and TV norm-based CS algorithms for signal recovery and image reconstruction, experiments demonstrated that the ReSL0 algorithm provides a good balance between the quality and efficiency of reconstructions. Furthermore, the PSNR of the reconstructed image calculated by the introduced method was better than the other three methods. In particular, it can notably improve reconstruction quality in the case of noisy measurement.
Highlights
Photoacoustic imaging (PAI) is a new noninvasive and nonionizing biomedical imaging method which gains rapid development in the last two decades [1,2,3,4]
The reconstruction algorithm is an important factor affecting the quality of PAI imaging, and the accurate and efficient reconstruction algorithms are of great significance
We studied the use of the regularized smooth L0 (ReSL0) for L0 norm minimization Compressed sensing (CS) problems caused by sparse-view PAI reconstruction
Summary
Photoacoustic imaging (PAI) is a new noninvasive and nonionizing biomedical imaging method which gains rapid development in the last two decades [1,2,3,4]. The iterative reconstruction algorithm that gives a more accurate result at the expense of much more computing time has been developed for sparse-view PAI [17,18,19]. Initialization 1) Set J, L, μ > 0, and bθ0 = AHðAAHÞ−1y. The above studies indicated that the CS methods can reduce the number of ultrasonic transducers and get high-quality reconstruction results with sparse-view data. Based on the SL0 algorithm, Bu et al have adopted a regularization term to tolerate errors which can achieve the same computational efficiency as the SL0 algorithm while having better noise robustness This inspired us to apply the regularized smooth L0 (ReSL0) algorithm to reconstruct the sparseview PAI image. For the purpose of verifying the capability of the ReSL0 algorithm, the ReSL0 algorithm compares with three L0 norm, L1 norm, and TV norm-based CS algorithms for signal recovery and image reconstruction
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