Deep learning-based photoacoustic imaging of vascular network through thick porous media

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.