Medical image reconstruction methods based on deep learning have recently demonstrated powerful performance
in photoacoustic tomography (PAT) from limited-view and sparse data. However, because most of these
methods must utilize conventional linear reconstruction methods to implement signal-to-image transformations,
their performance is restricted. In this paper, we propose a novel deep learning reconstruction approach that
integrates appropriate data pre-processing and training strategies. The Feature Projection Network (FPnet)
presented herein is designed to learn this signal-to-image transformation through data-driven learning rather
than through direct use of linear reconstruction. To further improve reconstruction results, our method integrates
an image post-processing network (U-net). Experiments show that the proposed method can achieve
high reconstruction quality from limited-view data with sparse measurements. When employing GPU acceleration,
this method can achieve a reconstruction speed of 15 frames per second.