A Comparative Study of Four Total Variational Regularization Reconstruction Algorithms for Sparse-View Photoacoustic Imaging

Photoacoustic imaging (PAI) is a new nonionizing, noninvasive biomedical imaging technology that has been employed to reconstruct the light absorption characteristics of biological tissues. The latest developments in compressed sensing (CS) technology have shown that it is possible to accurately reconstruct PAI images from sparse data, which can greatly reduce scanning time. This study focuses on the comparative analysis of different CS-based total variation regularization reconstruction algorithms, aimed at finding a method suitable for PAI image reconstruction. The performance of four total variation regularization algorithms is evaluated through the reconstruction experiment of sparse numerical simulation signal and agar phantom signal data. The evaluation parameters include the signal-to-noise ratio and normalized mean absolute error of the PAI image and the CPU time. The comparative results demonstrate that the TVAL3 algorithm can well balance the quality and efficiency of the reconstruction. The results of this study can provide some useful guidance for the development of the PAI sparse reconstruction algorithm.