Photoacoustic imaging (PAI) suffers from inherent limitations that can degrade the quality of reconstructed results, such as noise, artifacts and incomplete data acquisition caused by sparse sampling or partial array detection. In this study, we proposed a new optimization method for both two-dimensional (2D) and three-dimensional (3D) PAI reconstruction results, called the regularized iteration method with shape prior. The shape prior is a probability matrix derived from the reconstruction results of multiple sets of random partial array signals in a computational imaging system using any reconstruction algorithm, such as Delay-and-Sum (DAS) and Back-Projection (BP). In the probability matrix, high-probability locations indicate high consistency among multiple reconstruction results at those positions, suggesting a high likelihood of representing the true imaging results. In contrast, low-probability locations indicate higher randomness, leaning more towards noise or artifacts. As a shape prior, this probability matrix guides the iteration and regularization of the entire array signal reconstruction results using the original reconstruction algorithm (the same algorithm for processing random partial array signals). The method takes advantage of the property that the similarity of the object to be imitated is higher than that of noise or artifact in the results reconstructed by multiple sets of random partial array signals of the entire imaging system. The probability matrix is taken as a prerequisite for improving the original reconstruction results, and the optimizer is used to further iterate the imaging results to remove noise and artifacts and improve the imaging fidelity. Especially in the case involving sparse view which brings more artifacts, the effect is remarkable. Simulation and real experiments have both demonstrated the superiority of this method.

翻译：光声成像（PAI）存在固有的局限性，可能降低重建结果的质量，例如由稀疏采样或部分阵列检测引起的噪声、伪影和不完整数据采集。本研究提出了一种适用于二维（2D）和三维（3D）PAI重建结果的新型优化方法，称为基于形状先验的正则化迭代方法。该形状先验是一个概率矩阵，它源自于在计算成像系统中使用任何重建算法（例如延时叠加（DAS）和反投影（BP））对多组随机部分阵列信号进行重建的结果。在该概率矩阵中，高概率位置表示多个重建结果在这些位置上具有高度一致性，表明其代表真实成像结果的可能性较高。相反，低概率位置则表示较高的随机性，更倾向于噪声或伪影。作为形状先验，该概率矩阵用于指导使用原始重建算法（即处理随机部分阵列信号时所用的相同算法）对整个阵列信号重建结果进行迭代和正则化。该方法利用了待成像物体在由整个成像系统的多组随机部分阵列信号重建出的结果中，其相似性高于噪声或伪影的特性。该概率矩阵被作为改进原始重建结果的前提条件，并利用优化器进一步迭代成像结果，以去除噪声和伪影，提高成像保真度。特别是在涉及稀疏视角（会带来更多伪影）的情况下，效果尤为显著。仿真和真实实验均证明了该方法的优越性。