  Photoacoustic (PA) imaging is a biomedical imaging modality capable of
acquiring high-contrast images of optical absorption at depths much greater
than traditional optical imaging techniques. However, practical instrumentation
and geometry limit the number of available acoustic sensors surrounding the
imaging target, which results in the sparsity of sensor data. Conventional PA
image reconstruction methods give severe artifacts when they are applied
directly to the sparse PA data. In this paper, we firstly propose to employ a
novel signal processing method to make sparse PA raw data more suitable for the
neural network, concurrently speeding up image reconstruction. Then we propose
Attention Steered Network (AS-Net) for PA reconstruction with multi-feature
fusion. AS-Net is validated on different datasets, including simulated
photoacoustic data from fundus vasculature phantoms and experimental data from
in vivo fish and mice. Notably, the method is also able to eliminate some
artifacts present in the ground truth for in vivo data. Results demonstrated
that our method provides superior reconstructions at a faster speed.
