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Photoacoustic (PA) imaging is a biomedical imaging modality capable ofacquiring high-contrast images of optical absorption at depths much greaterthan traditional optical imaging techniques. However, practical instrumentationand geometry limit the number of available acoustic sensors surrounding theimaging target, which results in the sparsity of sensor data. Conventional PAimage reconstruction methods give severe artifacts when they are applieddirectly to the sparse PA data. In this paper, we firstly propose to employ anovel signal processing method to make sparse PA raw data more suitable for theneural network, concurrently speeding up image reconstruction. Then we proposeAttention Steered Network (AS-Net) for PA reconstruction with multi-featurefusion. AS-Net is validated on different datasets, including simulatedphotoacoustic data from fundus vasculature phantoms and experimental data fromin vivo fish and mice. Notably, the method is also able to eliminate someartifacts present in the ground truth for in vivo data. Results demonstratedthat our method provides superior reconstructions at a faster speed.

AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data