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Photoacoustic imaging (PAI) has been widely investigated by researchers from a range of areas. According to the differences between excitation laser sources, PAI can be categorized into two main types, namely the time-domain and frequency-domain PAI. Although the frequency-domain approach is more portable and economic than the other alternative, the low intensity of excitation source may lead to a lower signal-to-noise ratio (SNR). This paper aiming to propose the suitable scheme for image reconstruction, we focus on the model-based PAI system and make great efforts to reduce the impact of noise algorithmically. Three regularization algorithms, i.e., Least Square QR-factorization, Tikhonov, and Total Variation minimization by Augmented Lagrangian and alternating direction algorithms (TVAL3) are studied. By choosing three important parameters as criteria, i.e., the peak SNR, image quality index, and time consumption during different situations, the most effective regularization algorithm amongst has been selected. Based on simulation results and detailed discussions, TVAL3 algorithm performs better than the other two for model-based signal reconstruction. The result is pivotal for effective PAI in high quality and highly efficient biomedical tomography and microimaging.

Investigation on Reconstruction for Frequency Domain Photoacoustic Imaging via TVAL3 Regularization Algorithm