Photoacoustic Microscopy Imaging from Acoustic Resolution to Optical Resolution Enhancement with Deep Learning

Photoacoustic Microscopy (PAM) optical resolution (OR) imaging method is suited to get high resolution bio-tissue image but suffers from shallow penetration depth. By contrast, photoacoustic acoustic resolution (AR) imaging has deeper penetration depth but with degraded imaging resolution. Inspired by the current advances in the field of deep neural network (DNN), we proposed a new DNN framework named Prior Residual U-Net (PRU-Net), which combines U-Net with global residual block and image prior for AR image to OR image resolution enhancement. It helps to aggregate the advantages of both imaging methods without the cost of building extra physical setup. By training the model with experimentally obtained OR image and simulated AR image pairs, the model is able to enhance the image quality from AR image towards OR image to a huge extent. The enhancement results of sub-images and complete image have both validated this method's effectiveness qualitatively and quantitatively.