In this paper, we describe an innovative PACT image reconstruction algorithm based on DI-Net, a dual-domain neural network. Both numerical simulations and in vivo experiments are used to evaluate the performance of the proposed DI-Net. The imaging results reveal that DI-Net can effectively suppress streak-type artifacts caused by undersampling and the reconstructed images are comparable with the reference image. The imaging results also demonstrate that the proposed DI-Net provides better image quality compared with the widely-used FBP algorithm and the popular Post-Unet algorithm.