标题： 带有形状先验正则化的光声迭代优化算法 显示英文标题

标题： Photoacoustic Iterative Optimization Algorithm with Shape Prior Regularization

摘要： 光电声成像（PAI）存在固有的局限性，这些局限性可能会降低重建结果的质量，例如由于稀疏采样或部分阵列检测导致的噪声、伪影和不完整的数据采集。 在本研究中，我们提出了一种新的优化方法，用于二维（2D）和三维（3D）PAI重建结果，称为具有形状先验的正则化迭代方法。 形状先验是一个概率矩阵，该矩阵是从计算成像系统中使用任何重建算法（如延迟与求和（DAS）和反投影（BP））对多组随机部分阵列信号的重建结果中得出的。 在概率矩阵中，高概率位置表示在这些位置上多个重建结果之间的一致性较高，表明这些位置更有可能代表真实的成像结果。 相反，低概率位置表示更高的随机性，更倾向于噪声或伪影。 作为形状先验，这个概率矩阵利用原始重建算法（处理随机部分阵列信号的相同算法）指导整个阵列信号重建结果的迭代和正则化。 该方法利用了被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的结果中，其相似性高于噪声或伪影的特性。 概率矩阵作为改进原始重建结果的前提条件，优化器用于进一步迭代成像结果以去除噪声和伪影并提高成像保真度。 特别是在涉及稀疏视角的情况，这种效果尤为显著。 仿真和真实实验都证明了该方法的优势。 显示英文摘要

摘要： 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 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.