Deep learning-powered biomedical photoacoustic imaging

Abstract

Photoacoustic Imaging (PAI) is an emerging hybrid imaging modality that combines optical imaging and ultrasound imaging, offering advantages such as high resolution, strong contrast, and safety. Despite demonstrating superior imaging capabilities, PAI still has certain limitations in its clinical application, such as the trade-off between imaging depth and spatial resolution, and the need for further improvement in imaging speed. Deep Learning, as a novel machine learning technique, has gained significant attention for its ability to improve medical image data and has been widely applied in PAI in recent years to overcome these limitations. In this review, we first introduce the principles of photoacoustic imaging, followed by the development and applications of popular deep neural network structures such as U-Net and GAN networks. Furthermore, we comprehensively discuss the recent advancements in the application of deep learning in photoacoustic imaging. Finally, a summary and discussion are provided.