High-quality three-dimensional (3D) photoacoustic imaging (PAI) is gaining increasing attention in clinical applications. To address the challenges of limited space and high costs, irregular geometric transducer arrays that conform to specific imaging regions are promising for achieving high-quality 3D PAI with fewer transducers. However, traditional iterative reconstruction algorithms struggle with irregular array configurations, suffering from high computational complexity, substantial memory requirements, and lengthy reconstruction times. In this work, we introduce SlingBAG Pro, an advanced reconstruction algorithm based on the point cloud iteration concept of the Sliding ball adaptive growth (SlingBAG) method, while extending its compatibility to arbitrary array geometries. SlingBAG Pro maintains high reconstruction quality, reduces the number of required transducers, and employs a hierarchical optimization strategy that combines zero-gradient filtering with progressively increased temporal sampling rates during iteration. This strategy rapidly removes redundant spatial point clouds, accelerates convergence, and significantly shortens overall reconstruction time. Compared to the original SlingBAG algorithm, SlingBAG Pro achieves up to a 2.2-fold speed improvement in point cloud-based 3D PA reconstruction under irregular array geometries. The proposed method is validated through both simulation and in vivo mouse experiments, and the source code is publicly available at https://github.com/JaegerCQ/SlingBAG_Pro.

翻译：高质量三维光声成像在临床应用中日益受到关注。为应对空间有限和成本高昂的挑战，贴合特定成像区域的不规则几何换能器阵列有望以更少的换能器实现高质量三维光声成像。然而，传统迭代重建算法难以适应不规则阵列配置，存在计算复杂度高、内存需求大、重建时间长等问题。本研究提出SlingBAG Pro算法，该算法基于滑动球自适应增长方法的点云迭代思想，并将其兼容性扩展至任意阵列几何。SlingBAG Pro在保持高重建质量、减少所需换能器数量的同时，采用结合零梯度滤波与迭代过程中逐步增加时间采样率的分层优化策略。该策略能快速去除冗余空间点云，加速收敛，并显著缩短整体重建时间。相较于原始SlingBAG算法，SlingBAG Pro在不规则阵列几何下将基于点云的三维光声重建速度提升了最高2.2倍。通过仿真与活体小鼠实验验证了所提方法的有效性，源代码已公开于https://github.com/JaegerCQ/SlingBAG_Pro。