Deep learning-boosted photoacoustic microscopy with an extremely low energy laser

Photoacoustic microscopy (PAM) is a high-resolution imaging modality capable of visualizing fine microvasculature in a biological tissue. Clinical translation of PAM system is still an issue due to the use of expensive and high energy laser sources for imaging. Although low energy laser sources can facilitate clinical transition of PAM systems as they are rugged, portable, affordable, and safe to use, the photoacoustic (PA) signal they generate is very weak, resulting in very low signal-to-noise ratio (SNR) PA signals and in turn low quality PA images. In this study, we have developed an enhancement autoencoder (EAE) utilizing fully convolutional neural network, that can improve the quality of the PA signals received, consequently improving the SNR of the reconstructed images. We acquired PAM data from leaf phantom with both high energy (target data) and low energy (input data) of the laser for training purposes and tested our trained model on PAM data obtained from leaf phantom. Our effort is to reconstruct the vascular structure as well as an accurate reading for the blood concentration. The latter has been neglected in the previous studies. The performance of our EAE is evaluated in terms of SNR, structural similarity index (SSIM), root mean square error (RMSE) and correlation.