High-quality 3D photoacoustic imaging (PAI) reconstruction under sparse view or limited view has long been challenging. Traditional 3D iterative-based reconstruction methods suffer from both slow speed and high memory consumption. Recently, in computer graphics, the differentiable rendering has made significant progress, particularly with the rise of 3D Gaussian Splatting. Inspired by these, we introduce differentiable radiation into PAI, developing a novel reconstruction algorithm: the Sliding Ball Adaptive Growth algorithm (SlingBAG) for 3D PAI, which shows ability in high-quality 3D PAI reconstruction both under extremely sparse view and limited view. We established the point cloud dataset in PAI, and used unique differentiable rapid radiator based on the spherical decomposition strategy and the randomly initialized point cloud adaptively optimized according to sparse sensor data. Each point undergoes updates in 3D coordinates, initial pressure, and resolution (denoted by the radius of ball). Points undergo adaptive growth during iterative process, including point destroying, splitting and duplicating along the gradient of their positions, manifesting the sliding ball effect. Finally, our point cloud to voxel grid shader renders the final reconstruction results. Simulation and in vivo experiments demonstrate that our SlingBAG reconstruction result's SNR can be more than 40 dB under extremely sparse view, while the SNR of traditional back-projection algorithm's result is less than 20 dB. Moreover, the result of SlingBAG's structural similarity to the ground truth is significantly higher, with an SSIM value of 95.6%. Notably, our differentiable rapid radiator can conduct forward PA simulation in homogeneous, non-viscous media substantially faster than current methods that numerically simulate the wave propagation, such as k-Wave. The dataset and all code will be open source.

翻译：在稀疏视角或有限视角下实现高质量的三维光声成像重建一直是一个挑战。传统的基于迭代的三维重建方法存在速度慢和内存消耗高的问题。最近，在计算机图形学领域，可微渲染技术取得了显著进展，尤其是随着三维高斯溅射的兴起。受此启发，我们将可微辐射引入光声成像，开发了一种新颖的重建算法：用于三维光声成像的滑动球自适应生长算法（SlingBAG）。该算法在极端稀疏视角和有限视角下均展现出高质量三维光声成像重建的能力。我们建立了光声成像的点云数据集，并采用了基于球面分解策略的可微快速辐射器，以及根据稀疏传感器数据自适应优化的随机初始化点云。每个点在三维坐标、初始压力和分辨率（由球半径表示）方面进行更新。在迭代过程中，点会根据其位置梯度进行自适应生长，包括点的销毁、分裂和复制，体现出滑动球效应。最后，我们的点云到体素网格着色器渲染出最终的重建结果。仿真和活体实验表明，在极端稀疏视角下，我们的SlingBAG重建结果的信噪比可超过40 dB，而传统反投影算法结果的信噪比则低于20 dB。此外，SlingBAG结果与真实情况的结构相似性显著更高，SSIM值达到95.6%。值得注意的是，我们的可微快速辐射器在均匀、非粘性介质中进行正向光声仿真的速度，远快于目前通过数值模拟波传播的方法（如k-Wave）。数据集和所有代码将开源。