Iterative optimization algorithm with structural prior for artifacts removal of photoacoustic imaging

In reality, photoacoustic imaging (PAI) is generally influenced by artifacts caused by sparse array or limited view. In this work, to balance the computing cost and artifact removal performance, we propose an iterative optimization method that does not need to repeat solving forward model for every iteration circle, called the regularized iteration method with structural prior (RISP). The structural prior is a probability matrix derived from multiple reconstructed images via randomly selecting partial array elements. High-probability values indicate high coherency among multiple reconstruction results at those positions, suggesting a high likelihood of representing true imaging results. In contrast, low-probability values indicate higher randomness, leaning more towards artifacts or noise. As a structural prior, this probability matrix, together with the original PAI result using all array elements, guides the regularized iteration of the PAI results. The simulation and real animal and human PAI study results demonstrated our method can substantially reduce image artifacts, as well as noise.