Simultaneous Algebraic Reconstruction Technique Based On Total Variation For Photoacoustic Image

Photoacoustic imaging (PAI) has unique structural and functional imaging capabilities and has attracted widespread attention in clinical diagnosis. However, in the case of fast or real-time imaging, the reconstruction of sparse-view sampling data of photoacoustic data is still a challenge. In this paper, we present our study on simultaneous algebraic photoacoustic reconstruction technique based on total variation. The proposed algorithm constructs an accurate projection matrix based on the detection sensitivity of the array element. Combining simultaneous algebraic reconstruction technique (SART) and total variation (TV) to optimize sparse-view sampling photoacoustic image reconstruction results. Numerical simulation experiment results show that the algorithm reconstructs high-quality photoacoustic images from sparse-view sampling data, effectively eliminates under-sampling artifacts, and preserves edge details. Compared with traditional algorithms, this algorithm may be a practical and effective algorithm for sparse-view PAI reconstruction.