Photoacoustic image reconstruction with the FD-UGAN model

Photoacoustic imaging has been widely used in the biomedical field by virtue of its high resolution and depth imaging advantages, but there are still problems with detector viewing angle limitation and incomplete data due to sparse sampling in the application process. To address this problem, FD-UGAN is proposed as a novel deep learning network, to our knowledge, in which the Full Dense U-Net is initially employed as the generator in a generative adversarial network framework for photoacoustic image reconstruction. The encoding and decoding pathways are redesigned to enable the network to efficiently capture and integrate multi-scale feature information, facilitating high-quality image reconstruction. The experimental results demonstrate that FD-UGAN outperforms existing reconstruction methods on the dataset. The proposed FD-UGAN provides an effective solution for image reconstruction from sparsely sampled data and exhibits significant potential for broader applications.