  Recent advances in photoacoustic (PA) imaging have enabled detailed images of
microvascular structure and quantitative measurement of blood oxygenation or
perfusion. Standard reconstruction methods for PA imaging are based on solving
an inverse problem using appropriate signal and system models. For handheld
scanners, however, the ill-posed conditions of limited detection view and
bandwidth yield low image contrast and severe structure loss in most instances.
In this paper, we propose a practical reconstruction method based on a deep
convolutional neural network (CNN) to overcome those problems. It is designed
for real-time clinical applications and trained by large-scale synthetic data
mimicking typical microvessel networks. Experimental results using synthetic
and real datasets confirm that the deep-learning approach provides superior
reconstructions compared to conventional methods.
