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.

通过使用扩散模型，利用 “DiffPam” 算法来加速光声成像过程。研究发现 DiffPam 在重构欠采样的光声显微镜图像方面具有可比性，并且无需大量数据集或深度学习模型的训练。这项研究强调了对具有有限人工智能专业知识和计算资源的研究人员来说，DiffPam 算法的重要性。