在光声断层成像中，通常利用超声换能器阵列接收光声信号，其制造成本较高，并且阵元数量对最终成像质量有重要影响。为了提升稀疏视角下光声重建的图像质量，提出了一种基于改进的U-Net神经网络结构的稀疏视角光声图像质量增强方法，该方法采用的改进的U-Net网络的特点在于通过添加连续卷积层替换跳接层，提升编码器和解码器拼接特征的匹配度；同时利用了基于多尺度结构相似性指数的损失函数对网络进行训练。基于仿体数据集和活体数据集的实验结果表明，改进的U-Net网络具有很好的图像细节重建能力，其所得的重建图像质量优于经典的U-Net网络。

In photoacoustic tomography, an ultrasonic transducer array is usually used to receive photoacoustic signals, which is expensive to manufacture, and the number of array elements has an important impact on the final imaging quality. To improve photoacoustic image quality reconstructed under sparse view conditoin, this study proposes a modified U-Net based on the replacement of the skip connection in a conventional U-Net with continuous convolutional layers, thereby increasing the matching degree of features transferred from the encoder to the decoder. Furthermore, the loss function based on the structural similarity index measure is used to train the network. Experimental results based on simulation and in vivo dataset show that compared with the conventional U-Net, the modified U-Net achieves more image details and the quality of the reconstructed image is significantly better.