Conventional reconstruction algorithms (e.g., delay-and-sum) used in photoacoustic imaging (PAI) provide a fast solution while many artifacts remain, especially for limited-view with ill-posed problem. In this paper, we propose a new convolutional neural network (CNN) framework Y-Net: a CNN architecture to reconstruct the initial PA pressure distribution by optimizing both raw data and beamformed images once. The network combines two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. We compared our result with some ablation studies, and the results of the test set show better performance compared with conventional reconstruction algorithms and other deep learning method (U-Net). Both in-vitro and in-vivo experiments are used to validated our method, which still performs better than other existing methods. The proposed Y-Net architecture also has high potential in medical image reconstruction for other imaging modalities beyond PAI.

光声成像（PAI）中使用的传统重建算法（例如延迟求和）提供了快速解决方案，但仍然存在许多伪影，特别是对于具有不适定问题的有限视图。在本文中，我们提出了一种新的卷积神经网络（CNN）框架 Y-Net：一种通过优化原始数据和波束形成图像一次来重建初始 PA 压力分布的 CNN 架构。该网络将两个编码器与一个解码器路径相结合，从而最佳地利用来自原始数据和波束形成图像的更多信息。我们将我们的结果与一些消融研究进行了比较，测试集的结果显示出与传统重建算法和其他深度学习方法（U-Net）相比更好的性能。体外和体内实验都用于验证我们的方法，该方法仍然比其他现有方法表现更好。所提出的 Y-Net 架构在 PAI 之外的其他成像模式的医学图像重建方面也具有巨大潜力。