Enhancement of structural and functional photoacoustic imaging based on a reference-inputted convolutional neural network

Photoacoustic microscopy has demonstrated outstanding performance in high-resolution functional imaging. However, in the process of photoacoustic imaging, the photoacoustic signals will be polluted by inevitable background noise. Besides, the image quality is compromised due to the biosafety limitation of the laser. The conventional approach to improving image quality, such as increasing laser pulse energy or multiple-times averaging, could result in more health risks and motion artifacts for high exposures to the laser. To overcome this challenge of biosafety and compromised image quality, we propose a reference-inputted convolutional neural network (Ri-Net). The network is trained using the photoacoustic signal and noise datasets from phantom experiments. Evaluation of the trained neural network demonstrates significant signal improvement. Human cuticle microvasculature imaging experiments are also conducted to further assess the performance and practicality of our network. The quantitative results show that we achieved a 2.6-fold improvement in image contrast and a 9.6 dB increase in signal-to-noise ratio. Finally, we apply our network, trained on single-wavelength data, to multi-wavelength functional imaging. The functional imaging of the mouse ear demonstrates the robustness of our method and the potential to capture the oxygen saturation of microvasculature. The Ri-Net enhances photoacoustic microscopy imaging, allowing for more efficient microcirculation assessments in a clinical setting.