Large-scale photoacoustic (PA) 3D imaging has become increasingly important for both clinical and pre-clinical applications. Limited by resource and application constrains, only sparsely-distributed transducer arrays can be applied, which necessitates advanced image reconstruction algorithms to overcome artifacts caused by using back-projection algorithm. However, high computing memory consumption of traditional iterative algorithms for large-scale 3D cases is practically unacceptable. Here, we propose a point cloud-based iterative algorithm that reduces memory consumption by several orders, wherein a 3D photoacoustic scene is modeled as a series of Gaussian-distributed spherical sources. During the iterative reconstruction process, the properties of each Gaussian source, including peak intensities, standard deviations and means are stored in form of point cloud, then continuously optimized and adaptively undergoing destroying, splitting, and duplication along the gradient direction, thus manifesting the sliding ball adaptive growth effect. This method, named the sliding Gaussian ball adaptive growth (SlingBAG) algorithm, enables high-quality 3D large-scale PA reconstruction with fast iteration and extremely less memory usage. We validated SlingBAG algorithm in both simulation study and in vivo animal experiments.

翻译：大规模三维光声成像在临床与临床前应用中日益重要。受资源与应用条件限制，通常仅能采用稀疏分布的换能器阵列，这需要先进的图像重建算法以克服反投影算法带来的伪影。然而，传统迭代算法在大规模三维场景中的高计算内存消耗在实际应用中难以接受。本文提出一种基于点云的迭代算法，可将内存消耗降低数个数量级。该算法将三维光声场景建模为一系列高斯分布的球面声源，在迭代重建过程中，每个高斯声源的峰值强度、标准差与均值以点云形式存储，并沿梯度方向持续优化，自适应地经历销毁、分裂与复制操作，从而呈现滑动球体自适应生长效应。这种命名为滑动高斯球自适应生长（SlingBAG）的算法，能够以快速迭代和极低内存消耗实现高质量的大规模三维光声重建。我们通过仿真研究与活体动物实验验证了SlingBAG算法的有效性。