Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy

In acoustic resolution photoacoustic microscopy (AR-PAM), a high numerical aperture focused ultrasound transducer (UST) is used for deep tissue high resolution photoacoustic imaging. There is a significant degradation of lateral resolution in the out-of-focus region. Improvement in out-of-focus resolution without degrading the image quality remains a challenge. In this work, we propose a deep learning-based method to improve the resolution of AR-PAM images, especially at the out of focus plane. A modified fully dense U-Net based architecture was trained on simulated AR-PAM images. Applying the trained model on experimental images showed that the variation in resolution is ∼10% across the entire imaging depth (∼4 mm) in the deep learning-based method, compared to ∼180% variation in the original PAM images. Performance of the trained network on in vivo rat vasculature imaging further validated that noise-free, high resolution images can be obtained using this method.