Research on ADMM Reconstruction Algorithm of Photoacoustic Tomography With Limited Sampling Data

Photoacoustic imaging is a new non-destructive biomedical imaging method. When limited independent data is available, the restoration of the initial pressure rise distribution is often an ill-posed problem. In this paper, based on the study of photoacoustic effects, the sparse prior information of photoacoustic images is integrated into the reconstruction process by using the compressed sensing (CS) theory and the L
²
norm optimization technique, combining the augmented Langrange weighting of the alternating direction method of multipliers (ADMM) with the total variation (TV) minimization problem, and the reconstruction artifacts are effectively eliminated. The simulation data from the real numerical model show that compared with the common time reversal algorithm, interpolation algorithm and truncated back projection algorithm, the total variational regularization method based on ADMM can effectively improve the quality of reconstructed images under the condition of limited viewing angles and incomplete projection data.