  Background: Photoacoustic Microscopy (PAM) integrates optical and acoustic
imaging, offering enhanced penetration depth for detecting optical-absorbing
components in tissues. Nonetheless, challenges arise in scanning large areas
with high spatial resolution. With speed limitations imposed by laser pulse
repetition rates, the potential role of computational methods is highlighted in
accelerating PAM imaging. Purpose: We are proposing a novel and highly
adaptable DiffPam algorithm that utilizes diffusion models for speeding up the
photoacoustic imaging process. Method: We leveraged a diffusion model trained
exclusively on natural images, comparing its performance with an in-domain
trained U-Net model using a dataset focused on PAM images of mice brain
microvasculature. Results: Our findings indicate that DiffPam achieves
comparable performance to a dedicated U-Net model, without the need for a large
dataset or training a deep learning model. The study also introduces the
efficacy of shortened diffusion processes for reducing computing time without
compromising accuracy. Conclusion: This study underscores the significance of
DiffPam as a practical algorithm for reconstructing undersampled PAM images,
particularly for researchers with limited AI expertise and computational
resources.
