High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network

Photoacoustic imaging is a powerful technique that provides high-resolution, deep tissue imaging. However, the time-intensive nature of photoacoustic microscopy (PAM) poses a significant challenge, especially when high-resolution images are required for real-time applications. In this study, we proposed an optimized Fast Residual Dense Generative Adversarial Network (FRDGAN) for high-quality PAM reconstruction. Through dataset validation on mouse ear vasculature, FRDGAN demonstrated superior performance in image quality, background noise suppression, and computational efficiency across multiple down-sampling scales (×4, ×8) compared to classical methods. Furthermore, in the in vivo experiments of mouse cerebral vasculature, FRDGAN achieves the improvement of 2.24 dB and 0.0255 in peak signal-to-noise ratio and structural similarity metrics in contrast to SRGAN, respectively. Our FRDGAN method provides a promising solution for fast, high-quality PAM microvascular imaging in biomedical research.