Learning-based Algorithm for Real Imaging System Enhancement: Acoustic Resolution to Optical Resolution Photoacoustic Microscopy

Abstract

Optical resolution photoacoustic microscopy (OR-PAM) imaging method can achieve high lateral resolution $(\lt 5 \mu \mathrm{m})$, while the penetration depth for OR is shallow (up to $1 \sim 2$ mm). In contrast, acoustic resolution photoacoustic microscopy (AR-PAM) imaging only has limited lateral resolution $(\gt 50 \mu \mathrm{m})$ but with deeper penetration depth up to several millimeters (3-10 mm). Enlighted by the recent progress in the field of machine learning, we proposed to enhance AR-PAM to OR-PAM while maintaining its high penetration depth merit with deep neural network, where a novel network structure named MultiResU-Net is employed. By training the network with OR images obtained with real setup and AR images simulated with physical model, the network is able to enhance the image quality of simulated AR image a huge extent that is similar to OR image. More importantly, the trained model is applied to real AR imaging system for both phantom and in vivo image enhancement. When compared with corresponding ground truth OR images, it can be fully substantiated that our proposed method realized the AR to OR target in real photoacoustic microscopy imaging system.