  Photoacoustic imaging (PAI) is a promising approach to realize in vivo
transcranial cerebral vascular imaging. However, the strong attenuation and
distortion of the photoacoustic wave caused by the thick porous skull greatly
affect the imaging quality. In this study, we designed a convolutional neural
network (CNN) with a U-Net architecture to extract the effective photoacoustic
information hidden in the speckle patterns; obtained vascular network images
datasets under porous media through simulation and experiment, and trained the
network weights respectively. The results show that the proposed neural network
can learn the mapping relationship between the speckle pattern and the target;
extract the photoacoustic signals of some vessels submerged in noise to
reconstruct high-quality images with a sharp outline of the vessel and clean
background. Compared with the traditional photoacoustic reconstruction method,
the deep learning-based reconstruction algorithms can achieve better
performance, the mean absolute error (MAE), Structural SIMilarity (SSIM) and
peak signal-to-noise ratio (PSNR) of reconstructed images have been greatly
improved. In conclusion, the proposed neural network can effectively extract
valid information from highly blurred speckle patterns for the rapid
reconstruction of target images, which offers promising applications in
transcranial photoacoustic imaging.
