Abstract: 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 sparsity of sensor data. Conventional PA image
reconstruction methods give severe artifacts when they are applied directly to
these sparse data. In this paper, we first employ a novel signal processing
method to make sparse PA raw data more suitable for the neural network, and
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 real data from in vivo fish and mice
imaging experiments. 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.