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.

翻译：背景：光声显微成像（PAM）融合了光学与声学成像技术，通过增强穿透深度实现对组织中光学吸收成分的检测。然而，在实现高空间分辨率的大面积扫描时仍面临挑战。受限于激光脉冲重复频率的速度约束，计算方法在加速PAM成像中的潜在作用日益凸显。目的：我们提出一种具有高度适应性的DiffPam算法，该算法利用扩散模型加速光声成像过程。方法：采用仅基于自然图像训练的扩散模型，并将其与使用小鼠脑部微血管PAM图像数据集进行领域内训练的U-Net模型进行性能比较。结果：研究表明，DiffPam可在无需大型数据集或训练深度学习模型的情况下，达到与专用U-Net模型相当的性能。本研究还证实了缩短扩散过程可在不牺牲精度的前提下有效降低计算耗时。结论：本研究凸显了DiffPam作为欠采样PAM图像重建实用算法的价值，尤其适用于人工智能专业知识有限且计算资源受限的研究人员。